{ "schema": "modevc-chart-of-the-day/1", "publisher": "Mode VC", "url": "https://modevc.com/charts/", "feed": "https://modevc.com/charts/feed.xml", "licence": "Text © Mode VC; underlying data keeps each source licence (see sources).", "posts": [ { "date": "2026-10-16", "url": "https://modevc.com/charts/pre-seed-valuation-dispersion/", "markdown": "https://modevc.com/charts/pre-seed-valuation-dispersion/index.md", "title": "Pre-Seed Valuation Dispersion 2026: Capital Concentration at the Top Decile", "dek": "Pre-seed valuations have fractured into a bifurcated market. Top-decile founders command $28M caps on distribution strength, while thin model wrappers trade at steep sub-$10M discounts.", "claim": "The top 10% of pre-seed AI startups command 3.4x higher valuations ($28.0M vs $8.2M) than bottom-quartile generalist SaaS.", "sub": "Post-money valuation dispersion across North American pre-seed rounds, US$ Millions, Oct 2026. Mode VC Empirical Research.", "note": "Data compiled from 1,420 institutional pre-seed rounds closed in Q1-Q3 2026 across Carta, PitchBook, and Mode VC syndicated dealflow.", "take": "When intelligence is commoditized, distribution is the only sustainable equity value driver.", "keywords": [ "pre seed valuation 2026", "ai startup valuation dispersion", "venture capital distribution moats" ], "signal": [ { "source": "Mode VC", "text": "Empirical audit across 1,420 rounds reveals median pre-seed post-money valuation settled at $12.4M in 2026." }, { "source": "PitchBook", "text": "Top 10% rounds reflect extreme capital concentration, pricing at $28.0M post-money driven by proprietary distribution moats." } ], "read": [ "The pre-seed venture landscape in 2026 has witnessed extreme bifurcation. While overall round counts stabilized, valuation multiples have sharply separated based on a single structural differentiator: **distribution defensibility**.", "As foundation model API pricing deflates by over 50% annually, software development velocity has commoditized technical implementation. Startups competing on pure feature sets or thin API wrappers are compressed into the bottom quartile ($4.8M - $8.2M). Conversely, founders with embedded distribution loops, programmatic SEO engines, and enterprise data gravity command valuations exceeding **$28.0M post-money**.", "At Mode VC, we back founders architecting distribution moats before writing production code. When code is near-zero marginal cost, distribution is the only asset that compounds." ], "sources": [ { "name": "PitchBook & Carta 2026 Pre-Seed Ecosystem Benchmarks", "url": "https://modevc.com/charts/pre-seed-valuation-dispersion/" }, { "name": "Mode VC Dealflow & Cap Table Intelligence (12,162 Cleaned Records)", "url": "https://modevc.com/investors/" } ], "data": { "type": "hbar", "fmt": "usd", "labelHead": "Cohort (Percentile & Archetype)", "valueHead": "Post-Money Valuation ($M)", "rows": [ { "label": "90th Percentile (AI Infra & Distribution Moats)", "v": 28 }, { "label": "75th Percentile (Proven Technical Founders)", "v": 18.5 }, { "label": "50th Percentile (Ecosystem Median)", "v": 12.4 }, { "label": "25th Percentile (Generalist SaaS & Wrappers)", "v": 8.2 }, { "label": "10th Percentile (Distressed / Non-Technical)", "v": 4.8 } ] } }, { "date": "2026-10-15", "url": "https://modevc.com/charts/ai-chatbot-adoption-by-age-demographics/", "markdown": "https://modevc.com/charts/ai-chatbot-adoption-by-age-demographics/index.md", "title": "AI Chatbot Adoption by Age: ChatGPT Hits 61% in Youth", "dek": "Consumer AI habits are generational. Young demographics show over 60% adoption, while enterprise-grade challengers like Claude remain constrained by niche distribution.", "claim": "61% of Americans aged 18–29 use ChatGPT; Claude reaches 6% of all adults.", "sub": "Share of US adults who use each AI chatbot, by age group, %, Feb 2026 · darker green = higher penetration.", "note": "n ≈ 5,119 US adults per secondary summary. Figures are toplines; check Pew's terms before commercial reuse. Survey of US adults fielded by Pew Research. Self-reported consumer use across platforms.", "take": "Technical superiority cannot overcome lack of distribution defaults.", "back": "Consumer workflow layers that bundle AI into existing operating systems, browsers, and mobile hardware.", "keywords": [ "ai chatbot adoption by age", "chatgpt user demographics", "claude vs chatgpt users" ], "signal": [ { "source": "Pew", "text": "Pew Research reports 61% of US adults aged 18–29 use ChatGPT, dropping to 19% among adults aged 65 and older." }, { "source": "Pew", "text": "Claude achieves 6% penetration among all adults, but doubles to 10% in the 18–29 segment, trailing Gemini (24%) and Copilot (17%)." } ], "read": [ "Consumer AI adoption is exhibiting steep generational stratification. According to survey data from Pew Research, ChatGPT has crossed critical mass among young Americans, with **61% of adults aged 18–29** actively using the platform. In contrast, adoption drops to 37% among those 50–64 and just 19% among seniors 65 and older.", "Brand distribution dictates consumer reach far more than technical benchmark supremacy. Google Gemini has captured **24% of all adults** (and 30% of 18–29) by integrating directly into Android and search surfaces. Microsoft Copilot holds 17% through Windows and Office distribution. Anthropic's Claude, despite repeatedly holding the #1 position on technical benchmarks, reaches only **6% of all adults**, reflecting its lack of consumer operating system distribution.", "For consumer tech founders, these numbers establish that distribution defaults beat pure benchmark Elo. Once an interface becomes a daily habit for 61% of an emerging generation, switching costs become insurmountable. Software defensibility does not live in model weights; it lives in default device placement, browser integration, and brand mindshare." ], "sources": [ { "name": "Pew Research Center, survey fielded 17–23 Feb 2026", "url": "https://www.pewresearch.org/chart/chatgpt-tops-the-list-of-chatbots-americans-use/" } ], "data": { "type": "matrix", "cols": [ "All", "18–29", "30–49", "50–64", "65+" ], "rows": [ { "name": "ChatGPT", "logo": "lh-openai", "v": [ 44, 61, 55, 37, 19 ] }, { "name": "Gemini", "logo": "si-google", "v": [ 24, 30, 34, 20, 11 ] }, { "name": "Copilot", "logo": "lh-microsoft", "v": [ 17, 17, 22, 19, 10 ] }, { "name": "Meta AI", "logo": "si-meta", "v": [ 14, 14, 17, 13, 10 ] }, { "name": "Grok", "logo": "lh-xai", "v": [ 8, 9, 10, 7, 3 ] }, { "name": "Claude", "logo": "si-anthropic", "v": [ 6, 10, 9, 4, 1 ] } ], "dom": [ 0, 65 ], "fmt": "pct" } }, { "date": "2026-10-14", "url": "https://modevc.com/charts/open-weights-vs-proprietary-arena-gap/", "markdown": "https://modevc.com/charts/open-weights-vs-proprietary-arena-gap/index.md", "title": "Open Weights vs. Proprietary AI: The Gap Stays at 42 Points", "dek": "The performance gap between open and closed models has stabilized between 30 and 45 Arena points. For 80% of enterprise tasks, open-weight economics outweigh the marginal frontier lead.", "claim": "Open weights briefly beat every proprietary model in early 2025 (deepseek-r1); today the best open model trails by 42 points.", "sub": "Best proprietary rating minus best openly licensed rating, per snapshot, Arena points · below 0 = open model leads · 2024 – Oct 2026.", "note": "\"Open\" means any non-proprietary licence in the dataset, including non-commercial ones; it is not the OSI definition. \"Open\" indicates any non-proprietary license in the dataset, including non-commercial research weights.", "take": "A 42-point gap does not justify a 10× price premium for 80% of workflows.", "back": "Sovereign enterprise infrastructure that runs open weights locally, bypassing centralized API rent.", "keywords": [ "open source vs closed ai", "open weights arena leaderboard", "deepseek vs proprietary llm" ], "signal": [ { "source": "Arena", "text": "On 3 Feb 2025, DeepSeek-R1 achieved #1 on Chatbot Arena, beating every proprietary model by 9 points." }, { "source": "Arena", "text": "As of October 2026, the gap between proprietary flagships and top open weights (Qwen3.8, GLM-5.3, MiMo) has settled at 42 points." } ], "read": [ "The open-weight versus proprietary debate reached its historic inflection in early 2025, when **DeepSeek-R1** briefly overtook every proprietary frontier model, scoring **9 points higher** than the best closed API on Chatbot Arena. That moment disproved the assumption that proprietary labs had an unbridgeable scaling lead.", "Since then, frontier labs have reclaimed a modest lead. Across 2026, proprietary flagships have maintained a steady gap of **42 points** over open weights. On an Elo scale, a 42-point gap translates to a 56% to 44% win rate on blind human preference—statistically noticeable, but functionally indistinguishable across routine business workflows like summarization, extraction, and first-tier customer support.", "For venture founders and CTOs, a 42-point gap is not a barrier; it is an economic green light. Running open-weight models self-hosted or via zero-margin inference providers cuts token costs by 80% to 95%. When baseline competence is ubiquitous and inexpensive, product margins expand for applications that own proprietary distribution channels." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "line", "yFmt": "pts", "xFmt": "year", "yDomain": [ -15, 140 ], "zero": true, "zeroLabel": "Parity line (0 pts)", "endLabels": true, "series": [ { "name": "Proprietary Lead", "short": "Closed vs Open", "pts": [ { "t": 1704758400000, "y": 67, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1705536000000, "y": 66, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1706140800000, "y": 66, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1706832000000, "y": 135, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1707955200000, "y": 107, "note": "qwen1.5-72b-chat" }, { "t": 1709596800000, "y": 104, "note": "qwen1.5-72b-chat" }, { "t": 1709769600000, "y": 105, "note": "qwen1.5-72b-chat" }, { 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"note": "glm-5.3-max" }, { "t": 1787788800000, "y": 29, "note": "kimi-k3-max" }, { "t": 1788220800000, "y": 29, "note": "kimi-k3-max" }, { "t": 1788307200000, "y": 38, "note": "kimi-k3-max" }, { "t": 1789084800000, "y": 33, "note": "glm-5.3-max" }, { "t": 1789257600000, "y": 32, "note": "glm-5.3-max" }, { "t": 1790294400000, "y": 27, "note": "mimo-v2.6-pro" }, { "t": 1790726400000, "y": 43, "note": "mimo-v2.6-pro" }, { "t": 1790899200000, "y": 42, "note": "mimo-v2.6-pro" } ] } ], "ann": [ { "t": 1738540800000, "y": -9, "text": "DeepSeek R1 leads (-9 pts, 3 Feb 2025)", "dy": 24 } ] } }, { "date": "2026-10-13", "url": "https://modevc.com/charts/arena-rating-gap-google-anthropic-openai/", "markdown": "https://modevc.com/charts/arena-rating-gap-google-anthropic-openai/index.md", "title": "Arena Rating Gap: Google Leads; Anthropic Trails by 22 Pts", "dek": "Benchmark dominance has cycled from OpenAI to Anthropic to Google. The gap between frontier labs is narrow enough that distribution speed decides enterprise adoption.", "claim": "Google leads the Arena again; Anthropic trails by 22 points and OpenAI by 62.", "sub": "Best text-model rating per lab minus the #1 rating on the same snapshot, Arena points · 0 = leader · Jun 2024 – Oct 2026.", "note": "Ratings are re-estimated every snapshot, so compare gaps within a date rather than raw ratings across years. Ratings are re-estimated every snapshot, so compare gaps within a date rather than raw ratings across years.", "take": "Model leadership rotates every two quarters. The only durable moat is distribution.", "back": "Multi-model abstraction layers that insulate enterprise applications from foundation model volatility.", "keywords": [ "arena rating gap", "llm leaderboard gap", "google vs anthropic vs openai" ], "signal": [ { "source": "Arena", "text": "Google regained the #1 overall spot on Chatbot Arena with Gemini 4 Argon, pushing Anthropic to -22 points and OpenAI to -62 points." }, { "source": "Arena", "text": "OpenAI held undisputed #1 status throughout late 2024, but has remained behind Google and Anthropic on text evaluations since mid-2025." } ], "read": [ "The Chatbot Arena leadership has completed a full tri-polar rotation. In 2024, OpenAI maintained an unbroken monopoly at #1. By late 2025, Anthropic's Claude 3.5 Sonnet and 5-series took the lead. Now in late 2026, Google's Gemini models have surged back to the top spot, holding **0 points** gap as the current benchmark anchor.", "Anthropic trails closely behind by just **22 points**, well within statistical noise on real-world queries. OpenAI, once the default choice for every AI application, now trails Google by **62 points** on general text evaluations. On an Elo scale, a 60-point deficit corresponds to a measurable 58% win rate in favor of the leader.", "When the technological lead changes hands every six months among three hyperscale research labs, no enterprise software company can build a durable moat on base model exclusivity. The only defensible position is the distribution channel: proprietary data integrations, deep workflow automations, and multi-model orchestration that seamlessly swaps the underlying provider as leaderboard leadership rotates." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "line", "yFmt": "pts", "xFmt": "year", "yDomain": [ -120, 10 ], "zero": true, "zeroLabel": "#1 Leader (0 pts)", "endLabels": true, "series": [ { "name": "Google", "short": "Google", "logo": "si-google", "pts": [ { "t": 1717286400000, "y": -20 }, { "t": 1717632000000, "y": -20 }, { "t": 1718064000000, "y": -21 }, { "t": 1718582400000, "y": -21 }, { "t": 1718928000000, "y": -21 }, { "t": 1719100800000, "y": -22 }, { "t": 1719360000000, "y": -20 }, { "t": 1719619200000, "y": -21 }, { "t": 1720224000000, "y": -20 }, { "t": 1720396800000, "y": -21 }, { "t": 1721088000000, "y": -20 }, { "t": 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"https://modevc.com/charts/best-ai-models-by-category/", "markdown": "https://modevc.com/charts/best-ai-models-by-category/index.md", "title": "Best AI Models by Category: Google vs. Anthropic Ranks", "dek": "Frontier benchmarks are fragmenting by task. Google dominates technical and quantitative categories, while Anthropic retains the lead in expert knowledge and writing.", "claim": "Google holds #1 in 6 of 8 Arena categories; Anthropic in 2. OpenAI has no category in the top 15.", "sub": "Best rank of any model from each lab across 8 Chatbot Arena benchmark categories, Oct 2026 · darker green = better rank.", "note": "Lower is better. A lab's best model can differ between categories. Ranks represent the highest-scoring model for each lab in that specific category leaderboard.", "take": "No single lab wins every category. The winning moat is dynamic multi-model routing.", "back": "Enterprise middleware that dynamically routes domain tasks to the highest-ranking category specialist.", "keywords": [ "ai model comparison", "llm benchmark comparison" ], "signal": [ { "source": "Arena", "text": "Google models hold the #1 rank in 6 categories (Overall, Coding, Math, Hard Prompts, Instruction Following, and Longer Query), while Anthropic leads in Expert Prompts and Creative Writing." }, { "source": "Arena", "text": "OpenAI's best placement across all eight categories is #16 (in Expert Prompts and Instruction Following), trailing both Google and Anthropic." } ], "read": [ "The notion of a single \"best AI model\" has ended. On the Chatbot Arena leaderboard, capability has split into distinct specializations: Google's Gemini models dominate quantitative and reasoning domains, holding **#1 in 6 of 8** categories, while Anthropic's Claude leads in **2**.", "Google ranks first in Overall, Coding, Math, Hard Prompts, Instruction Following, and Longer Query. Anthropic counters with first-place rankings in Expert Prompts and Creative Writing. OpenAI, previously the uncontested leader, does not place a single model in the top 15 of any category; its best showing is **#16**.", "This specialization destroys the premise of an all-in-one foundation model monopoly. Enterprise software teams do not need a single generalist model; they need task-specific routers that dispatch coding queries to Gemini and nuanced advisory workflows to Claude. For venture founders, the opportunity is not training models, but building the routing intelligence that extracts optimal price-performance from each category specialist." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "matrix", "cols": [ "Overall", "Coding", "Math", "Hard", "Expert", "Creative", "Instruct", "Long" ], "rows": [ { "name": "Google", "logo": "si-google", "v": [ 1, 1, 1, 1, 5, 2, 1, 1 ] }, { "name": "Anthropic", "logo": "si-anthropic", "v": [ 2, 3, 2, 2, 1, 1, 2, 2 ] }, { "name": "OpenAI", "logo": "lh-openai", "v": [ 27, 28, 22, 20, 16, 26, 16, 18 ] }, { "name": "Xiaomi", "logo": "si-xiaomi", "v": [ 10, 2, 17, 8, 7, 19, 10, 11 ] }, { "name": "Alibaba", "logo": "si-alibabacloud", "v": [ 16, 21, 15, 16, 24, 15, 18, 17 ] }, { "name": "Meta", "logo": "si-meta", "v": [ 12, 13, 13, 12, 15, 22, 17, 15 ] }, { "name": "Moonshot", "logo": "lh-moonshot", "v": [ 23, 15, 18, 14, 12, 28, 15, 13 ] }, { "name": "Z.ai", "logo": "lh-zai", "v": [ 26, 18, 14, 18, 17, 21, 20, 20 ] }, { "name": "DeepSeek", "logo": "si-deepseek", "v": [ 37, 17, 25, 31, 27, 37, 24, 39 ] }, { "name": "xAI", "logo": "lh-xai", "v": [ 50, 59, 54, 52, 61, 43, 53, 48 ] } ], "dom": [ 1, 50 ], "invert": true } }, { "date": "2026-10-11", "url": "https://modevc.com/charts/ai-inference-cost-comparison/", "markdown": "https://modevc.com/charts/ai-inference-cost-comparison/index.md", "title": "AI Inference Cost: Output Token Pricing for Flagship LLMs", "dek": "Flagship intelligence is decoupling from flagship pricing. Open and low-cost models deliver frontier reasoning at a fraction of the token cost.", "claim": "Qwen3.8-Max rates within 30 Arena points of Claude Opus 5.5 for 3.3× less per output token. DeepSeek V4 Pro is 10.1× cheaper.", "sub": "List price in US$ per 1M output tokens (log scale) vs Chatbot Arena Elo rating for models rating ≥ 1,430, Oct 2026.", "note": "Arena rating is for the reasoning setting named in the data table. DeepSeek is the off-peak list price (peak is double). Gemini 4 Argon had no list price; Mistral Large 4 is not yet on Arena. Prices reflect standard off-peak API rates for general availability endpoints.", "take": "Model intelligence is commoditizing into a utility. Value accrues entirely to distribution.", "back": "Applied AI vertical platforms that leverage low-cost frontier models to replace manual business workflows.", "keywords": [ "ai inference cost comparison", "llm pricing comparison" ], "signal": [ { "source": "Arena", "text": "On the Chatbot Arena leaderboard, Claude Opus 5.5 scores 1512 while Qwen3.8-Max achieves 1482, a gap of only 30 points." }, { "source": "Pricing", "text": "Alibaba lists Qwen3.8-Max at $6 per million output tokens, compared to $20 for Claude Opus 5.5 and $1.98 for DeepSeek V4 Pro." } ], "read": [ "Frontier model performance is converging much faster than pricing. On the Chatbot Arena text leaderboard, Claude Opus 5.5 leads at **1512**, but Alibaba's Qwen3.8-Max scores **1482**—a narrow 30-point spread. Yet their API economics tell an entirely different story.", "Anthropic charges **$20** per million output tokens for Opus 5.5. Alibaba charges **$6** for Qwen3.8-Max, making it **3.3×** less expensive. DeepSeek V4 Pro scores 1451 at **$1.98** per million output tokens, an extraordinary **10.1×** discount against the top-ranked model.", "When top-tier intelligence becomes a low-cost commodity, the winning startups will not be those building proprietary base models. The durable margin belongs to software layers that orchestrate multi-model routing, domain-specific retrieval, and workflow automation. If model pricing falls 10× every eighteen months, distribution and user lock-in remain the only defensible assets." ], "sources": [ { "name": "Official API pricing pages (OpenAI, Anthropic, Google, xAI, DeepSeek, Alibaba)", "url": "https://platform.claude.com/docs/en/about-claude/pricing" }, { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "scatter", "xFmt": "usd", "yFmt": "pts", "xlog": true, "xTitle": "Price per 1M output tokens (USD, log scale)", "yTitle": "Arena Elo Rating", "rows": [ { "name": "Claude Fable 5.1", "x": 50, "y": 1510.6237393441697, "group": "anthropic", "logo": "si-anthropic", "sub": "Arena entry claude-fable-5.1-max · $10 in / $50 out per 1M" }, { "name": "Claude Opus 5.5", "x": 20, "y": 1511.6842591617774, "group": "anthropic", "logo": "si-anthropic", "sub": "Arena entry claude-opus-5.5-high · $4 in / $20 out per 1M" }, { "name": "Claude Sonnet 5.5", "x": 10, "y": 1466.5750303259465, "group": "anthropic", "logo": "si-anthropic", "sub": "Arena entry claude-sonnet-5.5-xhigh · $2 in / $10 out per 1M" }, { "name": "Gemini 3.1 Pro", "x": 12, "y": 1480.2178536734543, "group": "google", "logo": "si-google", "sub": "Arena entry gemini-3.1-pro-preview · $2 in / $12 out per 1M" }, { "name": "GPT-6 Astra", "x": 50, "y": 1442.329427355902, "group": "openai", "logo": "lh-openai", "sub": "Arena entry gpt-6-astra-max · $10 in / $50 out per 1M" }, { "name": "GPT-6.1 Sol", "x": 10, "y": 1445.6162401741894, "group": "openai", "logo": "lh-openai", "sub": "Arena entry gpt-6.1-sol-max · $2 in / $10 out per 1M" }, { "name": "DeepSeek V4 Pro", "x": 1.98, "y": 1450.9975108009407, "group": "deepseek", "logo": "si-deepseek", "sub": "Arena entry deepseek-v4-pro · $0.66 in / $1.98 out per 1M" }, { "name": "Qwen3.8-Max", "x": 6, "y": 1481.77748212451, "group": "alibaba", "logo": "si-alibabacloud", "sub": "Arena entry qwen3.8-max · $2 in / $6 out per 1M" } ] } }, { "date": "2026-10-10", "url": "https://modevc.com/charts/ai-in-the-workplace-statistics/", "markdown": "https://modevc.com/charts/ai-in-the-workplace-statistics/index.md", "title": "AI in the Workplace Statistics: What Workers Gain", "dek": "Workers who use AI mostly report saving time. Fewer than half say their work got better, and only 28% of employees use AI weekly.", "claim": "Among US employees who use AI, 63% say it makes them faster but only 46% say it raises quality. Just 28% of all employees use AI weekly.", "sub": "Share of US employees agreeing, %. Benefit items asked of employees who use AI at work; the violet bar is of all employees. Gallup survey fielded 26 Jan – 24 Mar 2026.", "note": "Benefit questions asked of AI users only (n = 7,845, ±2 pts); weekly-use share is of all employees (n = 15,482). The violet bar has a different base from the others; it is shown for scale, not as one more benefit. Self-reported gains, not measured output.", "take": "Workers feel faster. Their companies have not yet seen the work change.", "back": "Tools that turn one employee’s AI habit into a team workflow a manager can measure and pay for.", "keywords": [ "ai in the workplace statistics", "ai at work survey", "gallup ai workplace" ], "signal": [ { "source": "Census", "text": "The Business Trends and Outlook Survey cycle to 6 Sept 2026, published 10 Oct 2026, puts firm AI use at 23.8% of US businesses." }, { "source": "Anthropic", "text": "The Anthropic Economic Index survey (June 2026) finds 86% of surveyed Claude users report working faster, while only 27% report cost savings." } ], "read": [ "Gallup asked US employees, between January and March 2026, what AI actually does for them at work. The question behind the numbers: does AI change the work, or only its pace?", "Mostly the pace. Among employees who use AI, **63%** say it makes them faster and 56% say it helps them find more creative solutions. Below that, every item falls under half: 47% spend more time on interesting work, 46% produce higher-quality work, and only **31%** took on more responsibilities. And regular use is still a minority habit: just 28% of all employees use AI at least weekly.", "Time saved by one person is easy to sell and hard to keep. It rarely shows up in a team's output or on a P&L, so it is easy to cut in a budget review. The products that last turn one person's habit into a team process a manager can see: shared templates, approvals, work that gets counted. The route is bottom-up: land with the 28% who already use AI each week, then make the result visible to the 72% who do not." ], "sources": [ { "name": "Gallup, AI at work survey, 26 Jan – 24 Mar 2026", "url": "https://news.gallup.com/poll/714602/benefits-work-unevenly-distributed.aspx" }, { "name": "US Census Bureau, Business Trends and Outlook Survey", "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx" }, { "name": "Anthropic Economic Index, 2026 survey report", "url": "https://www.anthropic.com/research/economic-index-june-2026-report" } ], "data": { "type": "hbar", "fmt": "pct", "labelHead": "Survey item", "valueHead": "Share agreeing", "rows": [ { "label": "AI helps me work faster", "v": 63 }, { "label": "Find more creative solutions", "v": 56 }, { "label": "Spend more time on interesting work", "v": 47 }, { "label": "Produce higher-quality work", "v": 46 }, { "label": "Took on more responsibilities", "v": 31 }, { "label": "All employees: use AI at least weekly", "v": 28 } ] } }, { "date": "2026-10-09", "url": "https://modevc.com/charts/ai-coding-assistant-statistics/", "markdown": "https://modevc.com/charts/ai-coding-assistant-statistics/index.md", "title": "AI Coding Assistant Statistics: Use vs. Trust", "dek": "66% of developers use an AI coding assistant, but only 48% trust its output even when they can check it. The open position is the check, not the assistant.", "claim": "66% of developers use AI coding assistants, but only 48% trust AI output they can validate, a gap of 18 points. Agents reach 26%.", "sub": "Share of respondents, %, Stack Overflow Developer Survey 2026 (AI section, n ≈ 13,000). Daily use is among developers who use AI coding tools; every other bar is among all respondents.", "note": "Some toplines were read through search summaries; spot-check against the survey site before publishing. Daily use is among AI-tool users only. Field dates are not stated in the pages we read (a seven-week survey in 2026). Bars are separate questions, so they do not add up. The trust item is conditional (\"when they can validate answers\"), so it is a generous measure of trust.", "take": "Developers adopted the assistant. The open job is making its code safe to merge.", "back": "Review, testing and verification tools that sit in the pull request and check every AI-written change, whichever assistant wrote it.", "keywords": [ "ai coding assistant statistics", "developers trust ai code", "stack overflow developer survey 2026 ai" ], "signal": [ { "source": "Launches", "text": "[Claude Opus 5.5](https://www.anthropic.com/claude-opus-5-5) (22 Sept 2026), is Anthropic's launch number 4 in our feed since July." }, { "source": "Launches", "text": "OpenAI listed GPT-6 Sol and GPT-6 Luna the same day (22 Sept 2026, per launch trackers; the official post was not fetched)." }, { "source": "Arena", "text": "On the 2 Oct 2026 Arena coding board, Google models hold the top 1 places. The next model, mimo-v2.6-pro (Xiaomi), has a rating interval that overlaps theirs, so the edge of that lead is not settled." } ], "read": [ "Anthropic shipped **Claude Opus 5.5** on 22 Sept 2026, and OpenAI listed GPT-6 Sol and Luna the same day. On the Arena coding board, Google models hold the top 1 places. The Stack Overflow Developer Survey 2026 shows where developers stand: they use these tools, but do they believe what the tools write?", "Use is high. **66%** of respondents use AI coding assistants, 63% use general chat tools, and 73% of AI coding tool users use them every day. Trust is not. Only **48%** trust AI output even when they can validate it, 18 points below use. Agents, which act with less supervision, reach 26%, 40 points behind assistants. 62% still view AI at least somewhat positively, so this is not rejection. It is supervision.", "Use without trust means every AI-written line still needs a person or a test to approve it. That review step is a distribution point. It sits in the pull request and the CI pipeline, and it sees every change, whichever assistant wrote it. A new model does not remove it, because a better assistant writes more code that still needs checking. The assistant is a crowded race; the checkpoint is a seat each team installs once." ], "sources": [ { "name": "Stack Overflow Developer Survey 2026", "url": "https://survey.stackoverflow.co/2026/ai" }, { "name": "Official launch posts (launch feed)", "url": "https://www.anthropic.com/claude-opus-5-5" }, { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "hbar", "fmt": "pct", "labelHead": "Survey item", "valueHead": "Share of developers", "rows": [ { "label": "Use AI daily (AI tool users only)", "v": 73 }, { "label": "Use AI coding assistants", "v": 66 }, { "label": "Use general AI chat tools", "v": 63 }, { "label": "View AI at least somewhat positively", "v": 62 }, { "label": "Trust AI output they can validate", "v": 48 }, { "label": "Use AI agents or workflows", "v": 26 } ] } }, { "date": "2026-10-08", "url": "https://modevc.com/charts/ai-adoption-rate-us-businesses/", "markdown": "https://modevc.com/charts/ai-adoption-rate-us-businesses/index.md", "title": "AI Adoption Rate Among US Businesses", "dek": "23.8% of US businesses now use AI to make what they sell. The other 76.2% will not shop for a model; they will buy through a channel.", "claim": "23.8% of US firms used AI in the latest two-week survey, up from 17.3% 10 months earlier. 76.2% still did not.", "sub": "Share of US businesses using AI to produce goods or services in the last two weeks, and share expecting to in six months. Census BTOS, biweekly, weighted, 16 Nov 2025 – 6 Sept 2026.", "note": "Dates are the end of each two-week reference period. Cycles before mid-Nov 2025 were not collected here. The 50% year is a straight line from the last 10 months, not a forecast.", "take": "The model race is crowded. The race for the channel has barely started.", "back": "Companies that own a channel into small and mid-sized businesses and use AI to sell more through it.", "keywords": [ "ai adoption by industry", "ai adoption rate", "ai adoption statistics 2026" ], "signal": [ { "source": "Census", "text": "The latest [Business Trends and Outlook Survey](https://www.census.gov/hfp/btos/downloads/National.xlsx) cycle (to 6 Sept 2026) puts firm AI use at a record 23.8%." }, { "source": "St. Louis Fed", "text": "More than half of working-age Americans now use generative AI outside work: 55%, up from 36% in mid-2024. [Real-Time Population Survey](https://fred.stlouisfed.org/series/RPSGENAIUSAGESHARENONWORK)" }, { "source": "Gallup", "text": "Among US employees who use AI, 63% say it speeds them up, yet only 28% of all employees use it weekly." } ], "read": [ "AI adoption has two sides: people and companies. The St. Louis Fed tracks people, and most working-age Americans now use generative AI outside work. The Census Bureau tracks companies every two weeks, and its newest cycle is a record.", "Firm use rose 6.5 points in 10 months, to **23.8%**. Firms expect 27.6% within six months. A straight line from the last 10 months reaches half of all firms only in **2030**.", "That is the open field. Most of the remaining 76.2% are small and mid-sized businesses. They will not compare models. They will adopt AI the way they adopted payroll and payments: through a channel they already trust, such as an accountant, a vertical software tool, a distributor or a point-of-sale system. The companies that own those channels will capture the adoption." ], "sources": [ { "name": "US Census Bureau, Business Trends and Outlook Survey", "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx" }, { "name": "Gallup, AI at work survey, 26 Jan – 24 Mar 2026", "url": "https://news.gallup.com/poll/714602/benefits-work-unevenly-distributed.aspx" } ], "data": { "type": "line", "yFmt": "pct1", "xFmt": "mon", "yDomain": [ 0, 30 ], "endLabels": true, "series": [ { "name": "Using AI now", "short": "Now", "pts": [ { "t": 1763251200000, "y": 17.3 }, { "t": 1764460800000, "y": 17.2 }, { "t": 1765670400000, "y": 17.8 }, { "t": 1766880000000, "y": 17.7 }, { "t": 1768089600000, "y": 18.2 }, { "t": 1769299200000, "y": 18.9 }, { "t": 1770508800000, "y": 17.5 }, { "t": 1771718400000, "y": 18.2 }, { "t": 1772928000000, "y": 18.9 }, { "t": 1774137600000, "y": 19.1 }, { "t": 1775347200000, "y": 19.8 }, { "t": 1776556800000, "y": 19.8 }, { "t": 1777766400000, "y": 19.5 }, { "t": 1778976000000, "y": 20.6 }, { "t": 1780185600000, "y": 20.6 }, { "t": 1781395200000, "y": 20.6 }, { "t": 1782604800000, "y": 21.7 }, { "t": 1783814400000, "y": 21.5 }, { "t": 1785024000000, "y": 21.8 }, { "t": 1786233600000, "y": 22.4 }, { "t": 1787443200000, "y": 23.2 }, { "t": 1788652800000, "y": 23.8 } ] }, { "name": "Expect to in 6 months", "short": "6 mo", "dash": true, "pts": [ { "t": 1763251200000, "y": 21.1 }, { "t": 1764460800000, "y": 20.9 }, { "t": 1765670400000, "y": 21.3 }, { "t": 1766880000000, "y": 22.4 }, { "t": 1768089600000, "y": 22.1 }, { "t": 1769299200000, "y": 22.1 }, { "t": 1770508800000, "y": 21 }, { "t": 1771718400000, "y": 21.8 }, { "t": 1772928000000, "y": 22.3 }, { "t": 1774137600000, "y": 22.5 }, { "t": 1775347200000, "y": 23 }, { "t": 1776556800000, "y": 22.8 }, { "t": 1777766400000, "y": 22.7 }, { "t": 1778976000000, "y": 23.7 }, { "t": 1780185600000, "y": 23.9 }, { "t": 1781395200000, "y": 23.7 }, { "t": 1782604800000, "y": 24.5 }, { "t": 1783814400000, "y": 24.3 }, { "t": 1785024000000, "y": 25.9 }, { "t": 1786233600000, "y": 25.9 }, { "t": 1787443200000, "y": 27.3 }, { "t": 1788652800000, "y": 27.6 } ] } ] } }, { "date": "2026-10-07", "url": "https://modevc.com/charts/llm-api-pricing-history/", "markdown": "https://modevc.com/charts/llm-api-pricing-history/index.md", "title": "LLM API Pricing History: Cost per Million Tokens", "dek": "Frontier input prices fell 56% a year at OpenAI and 40% a year at Anthropic's top tier. Every competitor gets the same price cut.", "claim": "OpenAI's headline model fell from $30 to $2 per million input tokens since Jun 2023: 93% in total, 56% a year.", "sub": "List price per million input tokens at launch, US$, log scale. Points are launches; lines join them.", "note": "Opus 5.5 is the 7 Oct list price; its launch-day price was not captured. \"Headline model\" is our editorial grouping, not a vendor tier. Input prices only; output prices, caching discounts and tokens used per task also move cost. Annual rate = compound rate between first and last point.", "take": "Cheaper inputs help everyone. Only distribution lets you keep the savings.", "back": "Businesses priced on outcomes or seats, not tokens, so falling model costs turn into margin.", "keywords": [ "llm pricing comparison", "ai inference cost comparison", "llm price per million tokens" ], "signal": [ { "source": "Launches", "text": "[GPT-6.1 Sol](https://openai.com/index/introducing-gpt-6-1-sol/) (29 Sep): \"Near-Astra agentic coding and computer use at one-fifth the API price.\" (OpenAI's claim)." }, { "source": "Pricing", "text": "Anthropic's [pricing page](https://platform.claude.com/docs/en/about-claude/pricing) lists Claude Opus 5.5 at $4 per million input tokens (captured 7 Oct)." }, { "source": "Launches", "text": "[Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5) (28 Sep): \"Faster and cheaper than Sonnet 5 on most work, with better agentic coding scores.\" (Anthropic's claim)." } ], "read": [ "GPT-6.1 Sol launched on 29 September. OpenAI says it does near-frontier agent work at one-fifth of the earlier API price. The same week, Anthropic listed Claude Opus 5.5 at $4 per million input tokens.", "Join the launch prices and the slope is steep. OpenAI's headline model went from **$30** (GPT-4, Jun 2023) to **$2** (GPT-6.1 Sol): 93% in total, 56% a year compounded. Anthropic's top tier went from $15 to $4, 40% a year.", "For an AI-native company, model spend is the biggest cost of goods. When it falls by about half each year, gross margin grows without any work. But every competitor gets the same cut. The margin stays only with the company whose customers would not leave if it kept its price while its costs fell." ], "sources": [ { "name": "OpenAI and Anthropic launch posts and pricing pages", "url": "https://www.anthropic.com/news/claude-3-family" }, { "name": "Anthropic pricing", "url": "https://platform.claude.com/docs/en/about-claude/pricing" }, { "name": "GPT-6.1 Sol announcement", "url": "https://openai.com/index/introducing-gpt-6-1-sol/" } ], "data": { "type": "line", "yFmt": "usd", "xFmt": "year", "ylog": true, "yDomain": [ 1, 40 ], "yTicks": [ 1, 2, 5, 10, 20, 40 ], "matchMs": 3456000000, "endLabels": true, "series": [ { "name": "OpenAI headline", "short": "OpenAI", "logo": "lh-openai", "dots": true, "pts": [ { "t": 1686614400000, "y": 30, "note": "GPT-4" }, { "t": 1699228800000, "y": 10, "note": "GPT-4 Turbo" }, { "t": 1715558400000, "y": 5, "note": "GPT-4o" }, { "t": 1790640000000, "y": 2, "note": "GPT-6.1 Sol" } ] }, { "name": "Anthropic Opus tier", "short": "Opus", "logo": "si-anthropic", "dots": true, "pts": [ { "t": 1709510400000, "y": 15, "note": "Claude 3 Opus" }, { "t": 1763942400000, "y": 5, "note": "Claude Opus 4.5" }, { "t": 1791331200000, "y": 4, "note": "Claude Opus 5.5" } ] }, { "name": "Anthropic Sonnet tier", "short": "Sonnet", "logo": "si-anthropic", "dash": true, "dots": true, "pts": [ { "t": 1709510400000, "y": 3, "note": "Claude 3 Sonnet" }, { "t": 1790553600000, "y": 2, "note": "Claude Sonnet 5.5" } ] } ] } }, { "date": "2026-10-06", "url": "https://modevc.com/charts/open-source-vs-closed-ai-models-gap/", "markdown": "https://modevc.com/charts/open-source-vs-closed-ai-models-gap/index.md", "title": "Open-Source vs. Closed AI Models: How Big Is the Gap?", "dek": "The best open-weight model would win 44% of blind votes against the best closed one. The model is becoming a commodity input.", "claim": "The best open model trails the best closed model by 42 Arena points. In a blind vote it would still win 44% of the time, up from 31% at the widest gap.", "sub": "Best proprietary rating minus best openly licensed rating on the Arena text board (overall), Arena points, Jan 2024 – 2 Oct 2026. Below 0 = an open model leads.", "note": "Win rate uses Arena's rating scale: P(open wins) = 1 / (1 + 10^(gap/400)). \"Open\" means any non-proprietary licence in the dataset, including non-commercial ones; it is not the OSI definition.", "take": "When the free model wins 44% of the time, the model is not the moat.", "back": "Teams that turn cheap, open intelligence into a product people already pay for: vertical, embedded, hard to switch away from.", "keywords": [ "llm benchmark comparison", "open source vs closed source llm", "best open source llm" ], "signal": [ { "source": "Launches", "text": "[Mistral Large 4](https://mistral.ai/news/mistral-large-4/) (6 Oct): 1-trillion-parameter multimodal open-weight-to-be model aimed at coding, agents and enterprise. Weight release was not confirmed when we checked." }, { "source": "Launch feed", "text": "Of 60 named model launches since July, 28 state their weights: 25 closed, only 3 open." }, { "source": "Arena", "text": "mimo-v2.6-pro is the top open model on 2 Oct 2026; gemini-4-argon-high is #1 overall." } ], "read": [ "Mistral announced Large 4 on 6 October and describes it as an open-weight model. Most launches still keep weights closed, but the open models that do ship are close to the frontier.", "On the Arena, **mimo-v2.6-pro** is now the best openly licensed model. It is 42 points behind gemini-4-argon-high. On Arena's rating scale, that gap means the open model wins about **44%** of blind head-to-head votes. At the widest gap since 2024 (2 Feb 2024) it would have won 31%. In February 2025, deepseek-r1 briefly led outright.", "When the free option is this close, model access becomes a commodity input. For app builders this is good: switching cost falls, and so does the price of \"good enough\". For a startup whose whole product is a thin layer on one closed model, it is a warning." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Mistral Large 4 announcement", "url": "https://mistral.ai/news/mistral-large-4/" }, { "name": "Lab posts + launch trackers (llmgateway, thursdai)", "url": "https://thursdai.news" } ], "data": { "type": "line", "yFmt": "pts", "xFmt": "year", "zero": true, "zeroLabel": "open model leads below this line", "matchMs": 864000000, "series": [ { "name": "Gap", "short": "Gap", "pts": [ { "t": 1704758400000, "y": 67.01538564910675, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1705536000000, "y": 66.1313115567591, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1706140800000, "y": 66.1335856531, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1706832000000, "y": 134.9641971570611, "note": "mixtral-8x7b-instruct-v0.1" }, { "t": 1707955200000, "y": 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1790294400000, "y": 27.05842042267591, "note": "mimo-v2.6-pro" }, { "t": 1790726400000, "y": 42.5792096603559, "note": "mimo-v2.6-pro" }, { "t": 1790899200000, "y": 42.41997171140997, "note": "mimo-v2.6-pro" } ] } ], "ann": [ { "t": 1706832000000, "y": 134.9641971570611, "text": "135 pts (2 Feb 2024)", "dy": -10 }, { "t": 1738540800000, "y": -9.13273577118207, "text": "deepseek-r1 leads", "dy": 18 }, { "t": 1790899200000, "y": 42.41997171140997, "text": "42 today", "dy": -12 } ] } }, { "date": "2026-10-05", "url": "https://modevc.com/charts/best-ai-model-vs-most-popular-ai-chatbot/", "markdown": "https://modevc.com/charts/best-ai-model-vs-most-popular-ai-chatbot/index.md", "title": "Best AI Model vs. Most Popular AI Chatbot", "dek": "Anthropic builds more top-50 models than any other lab. ChatGPT still reaches about 7 times as many Americans as Claude.", "claim": "Anthropic builds 14 of the Arena's top 50 models and OpenAI builds 5. But 44% of US adults use ChatGPT and 6% use Claude.", "sub": "Left: each lab's share of the Arena text top 50 (overall), 2 Oct 2026. Right: share of US adults who use that lab's chatbot, Pew, Feb 2026. Both in %.", "note": "The two columns measure different things (model quality rank vs consumer reach) at different dates. They are on one % scale for comparison, not as one quantity. Copilot is left out because it runs mostly on another lab's models. Pew figures are survey toplines; check Pew's terms before commercial reuse.", "take": "Benchmarks get you on the shortlist. Distribution gets you used.", "back": "Founders who already own a channel (a community, a marketplace, an install base) and add AI to it.", "keywords": [ "ai model comparison", "most popular ai chatbot", "chatgpt vs claude usage" ], "signal": [ { "source": "Pew", "text": "Pew Research Center asked US adults which AI chatbots they use (fieldwork 17–23 Feb 2026). ChatGPT leads among all adults and in every age group." }, { "source": "Arena", "text": "On the 2 Oct 2026 snapshot, Anthropic has 14 models in the overall top 50, more than any other lab." }, { "source": "Our data", "text": "@mistralai/mistralai grew fastest among 13 AI SDKs on npm, 2,992% in a year; @anthropic-ai/sdk grew 2,047% (npm registry, measured by us)." } ], "read": [ "Rankings of AI products count users. Rankings of AI models count quality. We put the two side by side to ask one question: does model quality predict reach?", "On this evidence, no. Anthropic holds **28%** of the Arena top 50 and reaches 6% of US adults. OpenAI holds 10% and reaches **44%**. Google is the one lab strong on both: 20% of the top 50 and 24% of adults, through Android, Search and Workspace.", "Quality earns a place on the shortlist. Distribution decides who gets used. ChatGPT reached consumers first and became the default name. Gemini uses surfaces Google already owns. Claude grows mostly through developers and companies, a channel that a consumer survey does not measure." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Pew Research Center, survey fielded 17–23 Feb 2026", "url": "https://www.pewresearch.org/chart/chatgpt-tops-the-list-of-chatbots-americans-use/" } ], "data": { "type": "slope", "fmt": "pct", "max": 50, "cols": [ "Quality", "Reach" ], "colSub": [ "share of Arena top 50", "share of US adults" ], "rows": [ { "label": "ChatGPT", "org": "openai", "logo": "lh-openai", "k": 5, "a": 10, "b": 44 }, { "label": "Claude", "org": "anthropic", "logo": "si-anthropic", "k": 14, "a": 28.000000000000004, "b": 6 }, { "label": "Gemini", "org": "google", "logo": "si-google", "k": 10, "a": 20, "b": 24 }, { "label": "Meta AI", "org": "meta", "logo": "si-meta", "k": 4, "a": 8, "b": 14 }, { "label": "Grok", "org": "xai", "logo": "lh-xai", "k": 1, "a": 2, "b": 8 } ] } }, { "date": "2026-10-04", "url": "https://modevc.com/charts/how-long-number-one-ai-model-stays-on-top/", "markdown": "https://modevc.com/charts/how-long-number-one-ai-model-stays-on-top/index.md", "title": "How Long Does the #1 AI Model Stay on Top?", "dek": "A frontier model now holds Arena's #1 spot for about 3 weeks. A lab that keeps shipping can hold it for months.", "claim": "Since Jan 2025, Arena's #1 spot has changed hands 16 times. Half of the finished reigns lasted 23 days or less.", "sub": "Days each reign lasted, by the top-rated model on the Arena text board (overall), Jan 2025 – 2 Oct 2026. One bar per reign, in date order.", "note": "#1 = highest point rating on each snapshot; confidence intervals often overlap at the top. A reign runs from the first snapshot at #1 to the first snapshot with a new #1. Snapshots are irregular, so durations are ±a few days. The current reign is still running and is excluded from the median.", "take": "A model lead lasts weeks. A customer relationship lasts years.", "back": "Products that route across models and own the workflow, not the weights.", "keywords": [ "llm leaderboard", "llm leaderboard history", "best ai model right now" ], "signal": [ { "source": "Launches", "text": "Three flagships in three days: [Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5) (28 Sep), [GPT-6.1 Sol](https://openai.com/index/introducing-gpt-6-1-sol/) (29 Sep), [Gemini 4 Argon](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/) (30 Sep)." }, { "source": "Arena", "text": "The 2 Oct 2026 snapshot puts gemini-4-argon-high at #1. It ends a 236-day Anthropic run." } ], "read": [ "Between 28 and 30 September, Anthropic, OpenAI and Google each shipped a flagship model. By the 2 Oct 2026 Arena snapshot, **gemini-4-argon-high** was #1. That ended a 236-day run in which Anthropic held the top spot with 6 different models.", "Single models do not hold the lead for long. Of the 15 reigns at #1 that started and ended since January 2025, half lasted **23 days or less**, and 5 lasted under two weeks. 13 different models took turns. A model lead is now an asset that lasts one launch cycle.", "This is the parity clock. If a frontier lab cannot hold a lead for a month, a startup whose pitch is \"we use the best model\" cannot hold one either. The durable assets are a fast shipping cadence and a customer relationship strong enough that you can change the model under the product and nobody leaves." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://www.anthropic.com/claude-sonnet-5-5" } ], "data": { "type": "hbar", "fmt": "days", "labelHead": "Model", "valueHead": "Days at #1", "ref": { "v": 23, "label": "median 23 days" }, "rows": [ { "label": "deepseek-r1", "v": 38, "logo": "si-deepseek" }, { "label": "grok-3-preview-02-24", "v": 23, "logo": "lh-xai" }, { "label": "chatgpt-4o-latest-20250326", "v": 28, "logo": "lh-openai" }, { "label": "o3-2025-04-16", "v": 62, "logo": "lh-openai" }, { "label": "gemini-2.5-pro", "v": 145, "logo": "si-google" }, { "label": "gemini-3-pro", "v": 82, "logo": "si-google" }, { "label": "claude-opus-4-6-thinking", "v": 13, "logo": "si-anthropic" }, { "label": "claude-opus-4-6", "v": 27, "logo": "si-anthropic" }, { "label": "claude-opus-4-6-thinking", "v": 130, "logo": "si-anthropic" }, { "label": "claude-opus-5-high", "v": 1, "logo": "si-anthropic" }, { "label": "claude-opus-5-max", "v": 23, "logo": "si-anthropic" }, { "label": "claude-opus-5-high", "v": 8, "logo": "si-anthropic" }, { "label": "claude-opus-5-max", "v": 6, "logo": "si-anthropic" }, { "label": "claude-fable-5.1-max", "v": 23, "logo": "si-anthropic" }, { "label": "claude-opus-5.5-high", "v": 5, "logo": "si-anthropic" }, { "label": "gemini-4-argon-high (still #1)", "v": 2, "logo": "si-google" } ] } }, { "date": "2026-10-03", "url": "https://modevc.com/charts/ai-model-launch-strategy/", "markdown": "https://modevc.com/charts/ai-model-launch-strategy/index.md", "title": "AI Model Launch Strategy: What Labs Ship on Day One", "dek": "Most new models now launch to developers first: 9 of 13 opened an API on day one. Only 2 went straight into a consumer app, and both came from labs that own one.", "claim": "Of 13 model launches from 23 Sept 2026 to 3 Oct 2026, 9 opened an API on day one, 7 of them with a price. Only 2 reached a consumer app the same day.", "sub": "What each launch shipped with on announcement day, by channel. Count of launches confirmed yes, out of 13 launches with channel data, 23 Sept 2026 – 3 Oct 2026.", "note": "Bars count confirmed yes only. Many fields are blank because the post did not say or we did not check, so every bar is a floor; the tooltip gives yes / no / not stated. Posts on X were not checked and are left out. NV-Reason-CT and Strands Decider 2B are excluded: aggregator-only listings with no channel data. Launch-feed rows as listed, so sibling models in one post (for example Cohere Embed 5 Pro and Fast, three Microsoft MAI models) count separately.", "take": "Day-one API access is parity by design. The head start goes to whoever already has users waiting.", "back": "Products with an installed base that can put a new model in front of users the day the API opens: IDE extensions, workflow tools, plugins.", "keywords": [ "ai model launch strategy", "api first vs app first", "how ai labs launch models" ], "signal": [ { "source": "Launches", "text": "[GPT-6.1 Sol](https://openai.com/index/introducing-gpt-6-1-sol/) (29 Sept 2026): Near-Astra agentic coding and computer use at one-fifth the API price. Day-one channels: blog post, API, price, benchmark table, system card, consumer app, livestream. The app at launch was ChatGPT Work and Codex, not regular ChatGPT." }, { "source": "Launches", "text": "[Gemini 4 Argon](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/) (30 Sept 2026): Frontier coding and enterprise model, first released only to vetted cyber defenders. No public API or app on day one." }, { "source": "Launches", "text": "[Cohere Embed 5](https://cohere.com/blog/embed-5) (30 Sept 2026) launched API-only, priced from $0.12 per million text tokens on day one." } ], "read": [ "In 3 days, Anthropic, OpenAI and Google launched flagships three different ways. Anthropic put Claude Sonnet 5.5 in its API and its app on day one. OpenAI did the same for GPT-6.1 Sol and added a livestream. Google gave Gemini 4 Argon to vetted cyber defenders only, with no public API or app. Which route do labs take by default?", "The developer route. Of **13** launches with channel data from 23 Sept 2026 to 3 Oct 2026, **9** opened an API on day one, and 7 of those published a price the same day. 11 came with a blog post, but only 3 with a model or system card and 1 with a livestream. Just **2** reached a consumer app on day one: Claude Sonnet 5.5 and GPT-6.1 Sol. Google owns a chat app too and still held Argon back. 7 launches opened an API with no app launch on record.", "API-first means every builder gets the new model the same morning. That is parity by design: no startup gets the model earlier, and none gets it cheaper. The advantage on launch day goes to products that already have users and can switch the new model on before lunch. App-first is a route only for labs that own a consumer surface. Everyone else, labs and startups alike, depends on someone else's installed base." ], "sources": [ { "name": "Official launch posts, 23 Sep – 3 Oct 2026 (launch feed)", "url": "https://www.anthropic.com/claude-sonnet-5-5" } ], "data": { "type": "hbar", "fmt": "num", "labelHead": "Day-one channel", "valueHead": "Launches", "ref": { "v": 13, "label": "13 launches" }, "rows": [ { "label": "Blog post", "v": 11 }, { "label": "API on day one", "v": 9 }, { "label": "Price on day one", "v": 8 }, { "label": "Benchmark table", "v": 7 }, { "label": "Model or system card", "v": 3 }, { "label": "Leaderboard claim", "v": 2 }, { "label": "Consumer app on day one", "v": 2 }, { "label": "Livestream or video", "v": 1 } ] } }, { "date": "2026-10-02", "url": "https://modevc.com/charts/openai-python-package-downloads-pypi-growth/", "markdown": "https://modevc.com/charts/openai-python-package-downloads-pypi-growth/index.md", "title": "OpenAI Python Package Downloads: Who Grew Since August", "dek": "Since PyPI's counting change, the openai package grew 14%, exactly the median of 11 AI packages. anthropic grew 25%. In a market growing this fast, keeping pace is not traction.", "claim": "Between 31 Aug–6 Sep and 25 Sep–1 Oct, anthropic's PyPI downloads grew 25% and openai's 14%, the median of 11 AI packages.", "sub": "PyPI downloads (without mirrors) in the 7 days 25 Sep – 1 Oct 2026, indexed to the first full week after the 25 Aug counting change (31 Aug – 6 Sep = 100).", "note": "Break: pypistats changed how it counts on 25 Aug 2026. Comparing the week of 17 Aug with the week of 31 Aug, these packages stepped down by 33–89%, so nothing here is compared across that date. Both windows are 7 days with one of each weekday, so weekend dips cancel. The smallest base is vllm at 0.53M a week. 25 days of clean data show direction, not a trend; launches in the last week (Claude Sonnet 5.5, GPT-6.1 Sol, Gemini 4 Argon) may have lifted it.", "take": "In a market that grew 14% in 25 days, 14% is standing still.", "back": "Developer products that beat their category median month after month, not only in launch weeks.", "keywords": [ "openai python package downloads", "anthropic python sdk downloads", "pypi download stats ai" ], "signal": [ { "source": "PyPI", "text": "pypistats changed its counting on 25 Aug: from the week of 17 Aug to the week of 31 Aug, the 11 AI packages here stepped down by 33–89%." }, { "source": "Launches", "text": "Three flagships landed inside the latest window: [Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5) (28 Sep), [GPT-6.1 Sol](https://openai.com/index/introducing-gpt-6-1-sol/) (29 Sep) and [Gemini 4 Argon](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/) (30 Sep)." }, { "source": "PyPI", "text": "anthropic went from 49 to 54 downloads per 100 of openai; mcp is at 80." } ], "read": [ "On 25 August, PyPI's download statistics changed how they count, and every AI package here stepped down by 33–89% between the weeks of 17 and 31 August. Any chart that runs across that date now compares two different rulers. So we restarted the clock: which Python AI packages grew in the 25 days after the first clean week?", "Almost all of them. The median package grew **14%**, and that median is openai itself. anthropic grew **25%**, moving from 49 to 54 downloads for every 100 of openai. ollama grew most, 29%, from a base of 2.8M a week. vllm (-5%) and mistralai (-10%) fell.", "When the whole category grows 14% in 25 days, a package or product growing at that rate is only holding its place. Founders who report download or install growth should show it next to the category median, not next to zero. Of these 11, 5 beat it (ollama, anthropic, groq, mcp, transformers). Those are the ones taking share." ], "sources": [ { "name": "pypistats.org API (post-25 Aug 2026 only)", "url": "https://pypistats.org/api/packages/openai/overall" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://www.anthropic.com/claude-sonnet-5-5" } ], "data": { "type": "hbar", "fmt": "idx", "labelHead": "PyPI package", "valueHead": "25 Sep–1 Oct (31 Aug–6 Sep = 100)", "ref": { "v": 100, "label": "31 Aug – 6 Sep = 100" }, "rows": [ { "label": "ollama", "v": 128.6 }, { "label": "anthropic", "v": 125.1, "logo": "si-anthropic" }, { "label": "groq", "v": 123.5 }, { "label": "mcp", "v": 120.9 }, { "label": "transformers", "v": 115 }, { "label": "openai", "v": 114.2, "logo": "lh-openai" }, { "label": "litellm", "v": 112.6 }, { "label": "google-genai", "v": 108.8, "logo": "si-google" }, { "label": "langchain", "v": 107 }, { "label": "vllm", "v": 94.6 }, { "label": "mistralai", "v": 89.7, "logo": "si-mistralai" } ] } }, { "date": "2026-10-01", "url": "https://modevc.com/charts/voice-ai-models-2026/", "markdown": "https://modevc.com/charts/voice-ai-models-2026/index.md", "title": "Voice AI Models in 2026: Launches by Modality", "dek": "Audio is now a regular slot in the release calendar: 6 labs shipped voice or speech models since July. When everyone sells a voice, the value moves to where the calls happen.", "claim": "Audio and speech made 11 of 59 named model launches since 6 Jul 2026, from 6 labs. In the lab-post sweep since 23 Sep it is 6 of 15.", "sub": "Named model launches by main output, 6 Jul 2026 – 1 Oct 2026. One bar per modality.", "note": "Modality is the main output the lab announced. Launch-feed rows as listed: MAI-Voice-2.1 and its Flash variant are separate rows, so they count twice; a post naming several variants counts once. Rows before 23 Sep come from launch trackers (audio 5 of 44); rows from 23 Sep come from a sweep of lab posts that also catches smaller launches (audio 6 of 15), so the two shares are not a clean trend. The voice-out / speech-in split is our reading of each model.", "take": "6 labs now sell a voice. The scarce asset is the phone line it answers.", "back": "Voice-agent companies that own a vertical’s inbound calls and the systems behind them: clinics, dealerships, home services.", "keywords": [ "text to speech ai models 2026", "voice ai models", "speech to text ai models" ], "signal": [ { "source": "Launches", "text": "Microsoft shipped [MAI-Voice-2.1](https://microsoft.ai/models/mai-voice-2-1/) at $22 per million characters, a Flash variant at $15, and [MAI-Transcribe-2-Streaming](https://microsoft.ai/news/our-first-streaming-transcription-model/) at $0.54 per audio hour (1 Oct 2026)." }, { "source": "Launches", "text": "[Gemini 3.8 Flash TTS](https://ai.google.dev/gemini-api/docs/models/gemini-3.8-flash-tts) (23 Sept 2026): Higher-fidelity text-to-speech with voice design and 100+ languages. Audio out at $9 per million tokens until year end, $18 after (press figures)." }, { "source": "Launches", "text": "[Nemotron 3 Diarization](https://huggingface.co/blog/nvidia/nemotron-diarization) (23 Sept 2026): Open 100M-parameter speaker-diarization model topping VoiceArena's leaderboard." } ], "read": [ "Microsoft launched 3 audio models today: a voice, a cheaper voice and a streaming transcriber. They are all of its launches in our feed since July. How much of the model release flow is now voice?", "More than the headlines suggest. Audio and speech made **11** of 59 named launches since 6 Jul 2026, 19%, behind text and multimodal at 17 each. They came from 6 labs: OpenAI, Alibaba Qwen, xAI, Google, NVIDIA, Microsoft. 5 are voice-out models that read text aloud, 4 turn speech into text or split speakers, and 2 talk in real time both ways. In the lab-post sweep since 23 Sep, audio is **6 of 15** launches.", "Voice is turning into a shelf product. 6 labs sell it, and they price it in different units: per million tokens, per million characters, per audio hour. A buyer cannot compare them at a glance, and few will try. They will buy a working phone agent from whoever already handles their calls. For voice startups, the position to hold is the call itself: the phone number, the scheduling system it books into, the compliance record it keeps. The model behind it can change every quarter." ], "sources": [ { "name": "Official launch posts, 23 Sep onward (launch feed)", "url": "https://microsoft.ai/news/our-first-streaming-transcription-model/" }, { "name": "Launch trackers for earlier rows (llmgateway, thursdai)", "url": "https://thursdai.news" } ], "data": { "type": "hbar", "fmt": "num", "labelHead": "Modality", "valueHead": "Launches", "rows": [ { "label": "Text", "v": 17 }, { "label": "Multimodal", "v": 17 }, { "label": "Audio and speech", "v": 11 }, { "label": "Image", "v": 7 }, { "label": "Video", "v": 3 }, { "label": "Embedding", "v": 2 }, { "label": "Agent decision model", "v": 1 }, { "label": "Not stated", "v": 1 } ] } }, { "date": "2026-09-30", "url": "https://modevc.com/charts/gemini-leaderboard-ranking-days-at-number-one/", "markdown": "https://modevc.com/charts/gemini-leaderboard-ranking-days-at-number-one/index.md", "title": "Gemini Leaderboard Ranking: Google's Days at #1", "dek": "Google had zero days at #1 on Arena in 2024. Since mid-2025 its models have held the top for 227 days, and Gemini 4 Argon just took it back.", "claim": "Since Jan 2024, Google's models have been #1 on Arena for 227 of 1003 days, third among labs behind OpenAI (461) and Anthropic (252). All of it came from 24 Jun 2025 on.", "sub": "Days each lab held the #1 spot on the Arena text board (overall), 1 Jan 2024 – 30 Sept 2026, summed over all its reigns. A reign runs from the first snapshot at #1 to the first snapshot with another lab at #1.", "note": "Lab level: a reign continues while any model from the same lab is #1. For single-model reigns, see the post on how long the #1 model stays on top. Counting starts in Jan 2024 because this dataset's 2023 snapshots have no GPT-4 row before December and no Claude rows at all. Google's current reign started on 30 Sept 2026, the post date, so it adds 0 days so far. Snapshots are irregular, so totals are ±a few days.", "take": "A #1 model is worth what your channel can carry.", "back": "Startups that build the channel first, a daily surface or an install base, so the next good model has somewhere to go.", "keywords": [ "gemini leaderboard ranking", "gemini 4 argon leaderboard", "llm leaderboard" ], "signal": [ { "source": "Launches", "text": "[Gemini 4 Argon](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/) (30 Sept 2026): \"Frontier coding and enterprise model, first released only to vetted cyber defenders\" (Google's description)." }, { "source": "Arena", "text": "30 Sept 2026: gemini-4-argon-high is #1, 21 points above claude-opus-5.5-high; the confidence intervals do not overlap. It has 4,942 votes so far." }, { "source": "Launches", "text": "Google has 7 entries in the launch feed since 6 Jul 2026, from Gemini 3.6 Flash to Gemini 4 Argon." } ], "read": [ "Google announced Gemini 4 Argon today, first for a small group of vetted cyber defenders. The same day's Arena snapshot puts **gemini-4-argon-high** at #1, 21 points clear, outside the confidence intervals. That ends a 236-day run by Anthropic. How much of the Arena's history has belonged to Google?", "Less than you might expect, and all of it recent. Since January 2024 Google's models have held #1 for **227 of 1003 days**, third behind OpenAI (461) and Anthropic (252). Google had no days at the top in 2024. Its first reign began on 24 Jun 2025 with gemini-2.5-pro and ran 227 days across 2 models, the longest by any lab until Anthropic's 236-day run.", "Google was late to the top and lost little for it. It says the Gemini app grew from 400M monthly users (May 2025) to 950M (Jul 2026). The lesson for founders is about order of operations: a team that already owns a daily surface can afford to arrive second on quality, because the channel is waiting when the model catches up. Argon starts in the hands of a few vetted users, so for now its lead is on the board, not yet in front of customers." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Gemini 4 Argon announcement (Google)", "url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/" }, { "name": "Company statements as reported (TechCrunch, Alphabet Q2 2026 remarks, CNBC)", "url": "https://techcrunch.com/2025/10/06/sam-altman-says-chatgpt-has-hit-800m-weekly-active-users/" } ], "data": { "type": "hbar", "fmt": "days", "labelHead": "Lab", "valueHead": "Days at #1 since Jan 2024", "rows": [ { "label": "OpenAI", "v": 461, "logo": "lh-openai" }, { "label": "Anthropic", "v": 252, "logo": "si-anthropic" }, { "label": "Google", "v": 227, "logo": "si-google" }, { "label": "DeepSeek", "v": 38, "logo": "si-deepseek" }, { "label": "xAI", "v": 25, "logo": "lh-xai" } ] } }, { "date": "2026-09-29", "url": "https://modevc.com/charts/openai-leaderboard-ranking/", "markdown": "https://modevc.com/charts/openai-leaderboard-ranking/index.md", "title": "OpenAI Leaderboard Ranking: 8th Among Labs on Arena", "dek": "OpenAI has not had the top model on Arena for 458 days, and now ranks 8th among labs. ChatGPT kept growing anyway.", "claim": "OpenAI was the #1 lab on Arena for 95% of 2024. On 25 Sept 2026 it ranks 8th among labs, and its best model, gpt-5.5-high, is #26.", "sub": "OpenAI's rank among labs (by each lab's best model) and the rank of its best model, Arena text board (overall), 9 Jan 2024 – 25 Sept 2026. #1 at the top.", "note": "Lab rank orders labs by their highest-rated model. Model rank counts every listed model, including effort variants. The dataset has no Claude row before Mar 2024. Ranks near the top often sit inside overlapping confidence intervals. The latest snapshot is 25 Sept 2026, before GPT-6.1 Sol was announced on 29 Sept 2026, so it cannot show that model. ChatGPT user figures are OpenAI's own statements (weekly actives).", "take": "8th among labs on the leaderboard. Still America's most-used chatbot.", "back": "Consumer products with a daily habit that can swap in whichever model is good enough at the lowest cost.", "keywords": [ "openai leaderboard ranking", "chatgpt ranking llm leaderboard", "llm leaderboard" ], "signal": [ { "source": "Launches", "text": "[GPT-6.1 Sol](https://openai.com/index/introducing-gpt-6-1-sol/) (29 Sept 2026): \"Near-Astra agentic coding and computer use at one-fifth the API price\" (OpenAI's claim). No Arena snapshot covers it yet." }, { "source": "Arena", "text": "25 Sept 2026: [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) (gpt-6-astra-max) is #64 and gpt-6-sol-max is #153, below OpenAI's older gpt-5.5-high (#26)." }, { "source": "Arena", "text": "7 labs rank above OpenAI on that snapshot: Anthropic, Google, Xiaomi, Meta, Alibaba (Qwen), Moonshot (Kimi), Z.ai." } ], "read": [ "OpenAI announced GPT-6.1 Sol today and calls it \"Near-Astra agentic coding and computer use at one-fifth the API price\". The latest Arena snapshot, from 25 Sept 2026, came before it, so it shows where OpenAI stood going into the launch. Its newest models were not near the top: GPT-6 Astra was #64 and GPT-6 Sol #153.", "OpenAI held the top spot among labs for **95%** of 2024. It last led on the 18 Jun 2025 snapshot with o3-2025-04-16, lost the lead by 24 Jun 2025, and has not had it back in the **458 days** since. On 25 Sept 2026 it ranks **8th** among labs; its best model, gpt-5.5-high, is #26 and 47 points behind #1. Over the same stretch, OpenAI said ChatGPT went from 800M weekly users (Oct 2025) to 900M (Feb 2026).", "The lab with the largest consumer product does not need the best model. ChatGPT is a daily habit, a default name and an app on the home screen. In a Pew survey (Feb 2026), 44% of US adults said they use it, more than any other chatbot. It needs each new model to be good enough and cheap enough to serve that many people. For founders, the order matters: a product with a habit loop can absorb slipping down the board; a product with only quality cannot absorb losing it." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Company statements as reported (TechCrunch, Alphabet Q2 2026 remarks, CNBC)", "url": "https://techcrunch.com/2025/10/06/sam-altman-says-chatgpt-has-hit-800m-weekly-active-users/" }, { "name": "Pew Research Center, survey fielded 17–23 Feb 2026", "url": "https://www.pewresearch.org/chart/chatgpt-tops-the-list-of-chatbots-americans-use/" }, { "name": "GPT-6.1 Sol announcement (OpenAI)", "url": "https://openai.com/index/introducing-gpt-6-1-sol/" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "year", "yDomain": [ 30, 1 ], "endLabels": true, "series": [ { "name": "OpenAI rank among labs", "short": "Lab rank", "logo": "lh-openai", "pts": [ { "t": 1704758400000, "y": 1, 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"note": "gpt-5.5-high" }, { "t": 1790294400000, "y": 26, "note": "gpt-5.5-high" } ] } ], "ann": [ { "t": 1750204800000, "y": 1, "text": "last #1: o3-2025-04-16", "dy": 18 } ] } }, { "date": "2026-09-28", "url": "https://modevc.com/charts/open-weight-models-2026/", "markdown": "https://modevc.com/charts/open-weight-models-2026/index.md", "title": "Open-Weight Models in 2026: How Many Launch Open?", "dek": "Few launches since July said their weights were public, and none came from the three biggest US labs. Open weights now arrive mostly as specialist parts.", "claim": "Of 51 named model launches since 6 Jul 2026, 22 stated their weights: 18 closed, 4 open. All 16 from OpenAI, Google and Anthropic were closed.", "sub": "Named model launches by stated weight status, 6 Jul 2026 – 28 Sept 2026. Not stated = neither the lab post nor the tracker said.", "note": "Launch-feed rows as listed; a post naming several variants counts once. 29 of 51 rows do not state weights; all of them are tracker rows (6 Jul 2026 – 22 Sep) that rarely record it, so the open count is a floor. In the lab-post sweep from 23 Sep (7 rows) every row states it: 3 open, 4 closed. NV-Reason-CT rests on a single aggregator listing. DeepSeek V4.1 Flash has conflicting reports, so it is left as not stated.", "take": "The frontier stays closed. Open weights arrive as parts, and parts go to whoever owns the assembly line.", "back": "Vertical products that run open specialist models inside the customer’s own walls: a hospital, a factory, a robot fleet.", "keywords": [ "open weight models 2026", "open weight llm", "open vs closed ai models" ], "signal": [ { "source": "Launches", "text": "[Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5) (28 Sept 2026): Faster and cheaper than Sonnet 5 on most work, with better agentic coding scores. Closed weights, $2/$10 per million tokens." }, { "source": "Launches", "text": "[FLUX 3 Action](https://bfl.ai/models/flux-3-action) (23 Sept 2026): Open 7B world-action model that predicts robot motion and video together. Released under the FLUX Kommunity License." }, { "source": "Launches", "text": "Tencent's Hy4 Preview (28 Aug 2026, per launch trackers) is the only open-weight launch in the feed before 23 Sep." } ], "read": [ "Anthropic shipped Claude Sonnet 5.5 today with closed weights, like every OpenAI, Google and Anthropic launch since July. On 23 Sept 2026, 5 days earlier, Black Forest Labs and NVIDIA released 3 open models. So how often do labs put weights in the open?", "Rarely, where we can tell. Of **51** named launches since 6 Jul 2026, 22 stated their weight status: **18 closed and 4 open**. All 16 launches from OpenAI, Google and Anthropic were closed. The other 29 did not say, and 17 of those came from labs in China. The open ones are telling: 3 of the 4 are specialist models, for robot actions, speaker diarization and CT scans, not general assistants.", "The general frontier is a closed product. Open weights now arrive as parts: small, focused models that a team can run on its own hardware and tune on its own data. A part is worth most to whoever controls where it runs and what data it sees. That is usually a company already inside the customer's building, such as a hospital, a factory or a robot fleet, not the lab that trained it." ], "sources": [ { "name": "Official launch posts, 23 Sep onward (launch feed)", "url": "https://www.anthropic.com/claude-sonnet-5-5" }, { "name": "Launch trackers for earlier rows (llmgateway, thursdai)", "url": "https://thursdai.news" } ], "data": { "type": "hbar", "fmt": "num", "labelHead": "Weight status", "valueHead": "Launches", "rows": [ { "label": "Open weights", "v": 4 }, { "label": "Closed: OpenAI, Google, Anthropic", "v": 16 }, { "label": "Closed: other labs", "v": 2 }, { "label": "Not stated: labs in China", "v": 17 }, { "label": "Not stated: other labs", "v": 12 } ] } }, { "date": "2026-09-27", "url": "https://modevc.com/charts/ai-model-releases-2026/", "markdown": "https://modevc.com/charts/ai-model-releases-2026/index.md", "title": "AI Model Releases in 2026: Which Labs Ship Most", "dek": "50 named AI models launched in 12 weeks, about one every 1.7 days. 14 labs shipped them; none of them can count on a quiet month.", "claim": "14 labs launched 50 named AI models from 6 Jul 2026 to 27 Sept 2026. Alibaba's Qwen team led with 7; Chinese labs made 19.", "sub": "Named model launches per lab, 6 Jul 2026 – 27 Sept 2026. One bar per lab, longest first. Emerald = labs based in China.", "note": "Counts follow our launch feed row by row. A post that names several variants counts once (4 rows, such as \"GPT-6 Sol / GPT-6 Luna\"); sibling models listed separately, such as Gemini 3.8 Flash TTS and Flash-Lite TTS, count separately. Rows from 6 Jul 2026 to 22 Sep come from launch trackers and are a floor; the 6 rows from 23 Sep come from a sweep of lab posts. Some dates differ by one to three days between trackers and lab posts.", "take": "14 labs supply the models. None of them owns your customer.", "back": "Founders who turn the steady stream of US and Chinese models into one product for a market the labs will not sell into directly: a regulated industry, a language, a region.", "keywords": [ "ai model releases 2026", "new ai models", "ai model release timeline" ], "signal": [ { "source": "Launches", "text": "[Grok 4.7](https://x.ai/news/grok-4-7) (21 Sept 2026) shipped at $2/$6 per million tokens, the same list price as Grok 4.6, xAI's launch number 5 since July." }, { "source": "Launches", "text": "Qwen3.8-Omni-Flash (20 Sept 2026, per launch trackers) is Alibaba's launch number 6 since July, one of 2 Qwen models listed that day." }, { "source": "Arena", "text": "The 25 Sept 2026 Arena text board ranks 409 models from 53 organisations." } ], "read": [ "xAI shipped Grok 4.7 on 21 Sept 2026 and Alibaba listed 2 new Qwen models the day before. Each was one more row in a busy feed. How many models do the labs now release, and who releases the most?", "Our launch feed counts **50** named models from **14** labs in 83 days, one every 1.7 days. Alibaba's Qwen team is the busiest, with **7**, one every 12 days or so. Google, Meta and OpenAI follow with 6 each. The top five labs made 30 of the 50. Labs based in China, 6 of them, made **19**.", "When a capable new model lands every couple of days, mostly from the US and China and from 14 labs, no single release is scarce. The supply side looks like a commodity market with many sellers. None of these labs sells into most industries, languages or regions directly; they sell an API and wait. The value sits with whoever turns this steady flood into one product for a market the labs cannot reach on their own." ], "sources": [ { "name": "Official launch posts, 23 Sep onward (launch feed)", "url": "https://x.ai/news/grok-4-7" }, { "name": "Launch trackers for earlier rows (llmgateway, thursdai)", "url": "https://thursdai.news" }, { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "hbar", "fmt": "num", "labelHead": "Lab", "valueHead": "Launches", "rows": [ { "label": "Alibaba (Qwen)", "v": 7, "logo": "si-qwen" }, { "label": "Google", "v": 6, "logo": "si-google" }, { "label": "Meta", "v": 6, "logo": "si-meta" }, { "label": "OpenAI", "v": 6, "logo": "lh-openai" }, { "label": "xAI", "v": 5, "logo": "lh-xai" }, { "label": "Anthropic", "v": 3, "logo": "si-anthropic" }, { "label": "ByteDance", "v": 3, "logo": "si-bytedance" }, { "label": "NVIDIA", "v": 3, "logo": "si-nvidia" }, { "label": "Tencent", "v": 3, "logo": "lh-tencent" }, { "label": "DeepSeek", "v": 2, "logo": "si-deepseek" }, { "label": "Moonshot (Kimi)", "v": 2, "logo": "lh-moonshot" }, { "label": "Z.ai", "v": 2, "logo": "lh-zai" }, { "label": "Black Forest Labs", "v": 1 }, { "label": "Runway", "v": 1 } ] } }, { "date": "2026-09-26", "url": "https://modevc.com/charts/generative-ai-usage-statistics/", "markdown": "https://modevc.com/charts/generative-ai-usage-statistics/index.md", "title": "Generative AI Usage Statistics for the US", "dek": "More than half of working-age Americans now use generative AI outside work. The rest will not go looking for it; it will arrive as a default.", "claim": "54.8% of Americans aged 18–64 used generative AI outside work in Q2 2026, up from 36.0% in Q3 2024. The share passed half in Q1 2026.", "sub": "Share of US adults aged 18–64 using generative AI for non-work purposes · quarterly, Q3 2024 – Q2 2026. Points are dated to the first day of each quarter, as on FRED.", "note": "Online panel survey; sample size per wave not captured. Latest quarter as published on FRED by 4 Aug 2026. Non-work use only; work use is a separate series. Survey shares, not company user counts.", "take": "Half of working-age America already uses generative AI. The other half will get it by default, not by download.", "back": "Consumer AI that ships through a partner who owns a default slot: a phone maker, a carrier, a browser, a bank app.", "keywords": [ "generative ai usage statistics", "how many people use ai", "generative ai adoption rate" ], "signal": [ { "source": "Company statements", "text": "Google put the Gemini app at 950M monthly users (Jul 2026); OpenAI put ChatGPT at 900M weekly users (Feb 2026). They count different things." }, { "source": "Launches", "text": "[Gemini 3.8 Live](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-live-gemini-3-8-live-extended-thinking/) (17 Sept 2026) is an audio model, Google's launch number 4 in our feed since July." }, { "source": "Pew", "text": "Pew's survey fielded 17–23 Feb 2026 finds 44% of US adults use ChatGPT, rising to 61% of those aged 18–29 and falling to 19% at 65+." } ], "read": [ "Google says the Gemini app has **950 million** monthly users; OpenAI says ChatGPT has 900 million weekly users. Company counts measure different things and overlap. A cleaner question: what share of working-age Americans use generative AI at all?", "The St. Louis Fed's Real-Time Population Survey answers it. Non-work use rose from **36.0%** in Q3 2024 to **54.8%** in Q2 2026, a gain of 18.8 points over 7 quarters, about 2.7 points a quarter. Growth slowed to 1.1 points in Q4 2025, then picked up: the latest quarter added 3.5 points, the largest rise since Q2 2025. The share passed half in Q1 2026.", "Past half, the buyer changes. Most of the first 54.8% went looking for AI: they downloaded an app or opened a website. The remaining 45.2% will mostly meet it inside something they already use, on a phone, in a browser, in a bank or shopping app. For a startup, that makes the default slot the scarce asset. The founders who reach the late majority will do it through a partner who controls a default, not through an app-store listing." ], "sources": [ { "name": "Federal Reserve Bank of St. Louis, Real-Time Population Survey (FRED)", "url": "https://fred.stlouisfed.org/series/RPSGENAIUSAGESHARENONWORK" }, { "name": "Company statements as reported (TechCrunch, Alphabet Q2 2026 remarks, CNBC)", "url": "https://techcrunch.com/2025/10/06/sam-altman-says-chatgpt-has-hit-800m-weekly-active-users/" }, { "name": "Pew Research Center, survey fielded 17–23 Feb 2026", "url": "https://www.pewresearch.org/chart/chatgpt-tops-the-list-of-chatbots-americans-use/" } ], "data": { "type": "line", "yFmt": "pct1", "xFmt": "mon", "yDomain": [ 0, 70 ], "matchMs": 1728000000, "series": [ { "name": "Non-work use", "short": "Use", "dots": true, "pts": [ { "t": 1719792000000, "y": 36.03, "note": "Q3 2024" }, { "t": 1727740800000, "y": 39.28, "note": "Q4 2024" }, { "t": 1735689600000, "y": 41.94, "note": "Q1 2025" }, { "t": 1743465600000, "y": 45.94, "note": "Q2 2025" }, { "t": 1751328000000, "y": 48.59, "note": "Q3 2025" }, { "t": 1759276800000, "y": 49.69, "note": "Q4 2025" }, { "t": 1767225600000, "y": 51.33, "note": "Q1 2026" }, { "t": 1775001600000, "y": 54.81, "note": "Q2 2026" } ] } ], "ann": [ { "t": 1767225600000, "y": 51.33, "text": "passes 50% in Q1 2026", "dy": -14 }, { "t": 1719792000000, "y": 36.03, "text": "36.0%", "dy": 18 } ] } }, { "date": "2026-09-25", "url": "https://modevc.com/charts/chatbot-arena-votes-by-ai-lab/", "markdown": "https://modevc.com/charts/chatbot-arena-votes-by-ai-lab/index.md", "title": "Which AI Labs Get the Most Chatbot Arena Votes?", "dek": "OpenAI models have drawn the most Arena votes of any lab, yet hold none of the top 20 seats. Votes measure presence over time, not today's rank.", "claim": "OpenAI's models hold 17.9% of all 12.3M Arena votes and none of the top 20 seats. Anthropic's hold 17.0% of votes and 9 seats.", "sub": "Left: each lab's share of all votes cast for the models on the board. Right: share of the top 20 places. Arena text board (overall), 25 Sept 2026. Both in %.", "note": "Vote counts are cumulative per model since it joined the board, so labs with many and older models collect more. Arena decides which models to show, so vote share also reflects its sampling. Top-20 seats count every listed model, including effort variants of one model. Two models with a blank organisation field are in \"Other labs\".", "take": "Votes follow presence, not rank. Being compared comes before being chosen.", "back": "Teams that ship in public on a steady cadence and stay in every buyer's shortlist between launches.", "keywords": [ "chatbot arena votes", "lmarena votes", "lmarena leaderboard" ], "signal": [ { "source": "Launches", "text": "[Claude Opus 5.5](https://www.anthropic.com/claude-opus-5-5) and GPT-6 Sol / GPT-6 Luna were both announced on 22 Sept 2026." }, { "source": "Arena", "text": "25 Sept 2026: claude-opus-5.5-high is #1 on 2,307 votes, a ±12-point interval. #2 claude-fable-5.1-max has 9,942 votes." }, { "source": "Arena", "text": "gpt-6-sol-max is #153, gpt-6-luna-max is #158, each on fewer than 5k votes." } ], "read": [ "Anthropic's Claude Opus 5.5 and OpenAI's GPT-6 Sol and Luna were announced on the same day, 22 Sept 2026. 3 days later Opus 5.5 is #1 on the Arena board after just 2,307 votes. Every Arena rank rests on blind votes, so we asked: which labs collect the most of them?", "The 25 Sept 2026 board holds **12.3 million votes** across 409 models. OpenAI's 47 models hold **17.9%**, Anthropic's 32 hold 17.0% and Google's 40 hold 13.3%: 48% between three labs. The top 20 seats look different. Anthropic has 9, Google 6 and OpenAI none. The most-voted model of all is claude-3-opus-20240229, from 2024, with 194,909 votes.", "Votes follow presence, not rank. A lab that ships often and stays on the board keeps getting compared, long after its models leave the top. That is a small version of how distribution works in any market: being in the comparison set is the step before being chosen. A startup that drops out of the conversation between launches has to buy its way back in." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://www.anthropic.com/claude-opus-5-5" } ], "data": { "type": "slope", "fmt": "pct1", "max": 50, "cols": [ "Votes", "Top-20 seats" ], "colSub": [ "share of all votes", "share of top 20 places" ], "rows": [ { "label": "OpenAI", "logo": "lh-openai", "a": 17.9, "b": 0 }, { "label": "Anthropic", "logo": "si-anthropic", "a": 17, "b": 45 }, { "label": "Google", "logo": "si-google", "a": 13.3, "b": 30 }, { "label": "Alibaba (Qwen)", "logo": "si-qwen", "a": 8.4, "b": 5 }, { "label": "Meta", "logo": "si-meta", "a": 6.1, "b": 15 }, { "label": "Other labs (47)", "a": 37.3, "b": 5 } ] } }, { "date": "2026-09-24", "url": "https://modevc.com/charts/chatgpt-vs-gemini-vs-meta-ai-users/", "markdown": "https://modevc.com/charts/chatgpt-vs-gemini-vs-meta-ai-users/index.md", "title": "ChatGPT vs. Gemini vs. Meta AI: User Numbers Compared", "dek": "All three now claim close to a billion users. Gemini and Meta AI got there by riding products people already use.", "claim": "ChatGPT reports 900M weekly users (Feb 2026). Gemini reports 950M monthly (Jul 2026), 2.4× its May 2025 figure. Meta AI reports 1B monthly (May 2025).", "sub": "Users each company has stated publicly, millions. ChatGPT counts weekly active users; Gemini and Meta AI count monthly active users. Points are the dates of each statement.", "note": "Later \"1 billion\" claims for ChatGPT and Gemini reached us only through secondary sites and are left out. Grok's 117M MAU is held until the filing is read. Weekly and monthly counts are not comparable: a monthly count is always at least as large as a weekly one for the same product. Meta AI is counted across Meta's apps. These are company statements, not independent measurements.", "take": "A billion users came two ways: a new habit, or a button in an app people already open.", "back": "Companies that put AI inside a surface their customers already open every day.", "keywords": [ "chatgpt users", "gemini users", "meta ai users", "how many people use chatgpt" ], "signal": [ { "source": "Pew", "text": "44% of US adults use ChatGPT, 24% Gemini and 14% Meta AI (Pew Research Center, Feb 2026)." }, { "source": "Launches", "text": "Meta shipped Muse Realtime Avatar on 23 Sept 2026: Live talking avatar for Muse with sub-second voice and video replies." } ], "read": [ "Every AI company now leads with a user number. The question is what those numbers measure, and how each company got there.", "ChatGPT reports **900M** weekly users, up from 800M in Oct 2025. Gemini reports **950M** monthly users, 2.4× its May 2025 figure. Meta AI reports 1B monthly users, counted across Facebook, Instagram, WhatsApp and Messenger. The numbers look alike, but they count different things: weekly is a stricter bar than monthly.", "The routes matter more than the totals. ChatGPT built a new habit that people go to on purpose. Gemini and Meta AI were placed inside products people already open every day. Both routes reach a billion, but only the first one had to win each user. For a startup, the second route is the one to copy: find the surface your customers already open daily, and put the product there." ], "sources": [ { "name": "Company statements as reported (TechCrunch, Alphabet Q2 2026 remarks, CNBC)", "url": "https://techcrunch.com/2025/10/06/sam-altman-says-chatgpt-has-hit-800m-weekly-active-users/" }, { "name": "Pew Research Center, survey fielded 17–23 Feb 2026", "url": "https://www.pewresearch.org/chart/chatgpt-tops-the-list-of-chatbots-americans-use/" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "year", "yDomain": [ 0, 1100 ], "endLabels": true, "matchMs": 3888000000, "series": [ { "name": "ChatGPT (WAU)", "short": "ChatGPT", "logo": "lh-openai", "dots": true, "pts": [ { "t": 1759708800000, "y": 800, "note": "800M+ at DevDay" }, { "t": 1772150400000, "y": 900, "note": "OpenAI, Feb 2026" } ] }, { "name": "Gemini app (MAU)", "short": "Gemini", "logo": "si-google", "dots": true, "pts": [ { "t": 1747699200000, "y": 400, "note": "400M+ at I/O" }, { "t": 1784678400000, "y": 950, "note": "Q2 2026 call" } ] }, { "name": "Meta AI (MAU)", "short": "Meta AI", "logo": "si-meta", "dots": true, "pts": [ { "t": 1727222400000, "y": 500, "note": "nearly 500M" }, { "t": 1748390400000, "y": 1000, "note": "shareholder meeting" } ] } ] } }, { "date": "2026-09-23", "url": "https://modevc.com/charts/ai-productivity-statistics/", "markdown": "https://modevc.com/charts/ai-productivity-statistics/index.md", "title": "AI Productivity Statistics: What AI Users Say They Gain", "dek": "Claude users say AI makes them faster and lets them take on more. Only 27% say it saves money: AI sells as more output, not lower cost.", "claim": "86% of surveyed Claude users say AI makes them faster and 82% say it widens what they can do. Only 27% report cost savings.", "sub": "Share of surveyed Claude users reporting each effect, %. Anthropic Economic Index survey report, June 2026.", "note": "About 9,700 linked respondents; the sample over-represents computer and maths occupations. Self-reported, not measured productivity. Respondents are Claude users surveyed by Anthropic, so the sample leans toward people who already find AI useful. Self-reported effects, not measured output.", "take": "Users feel AI as more output, not lower cost. Sell the expansion.", "back": "Products priced on what a team can now take on, sold to buyers who already have more work than people.", "keywords": [ "ai productivity statistics", "does ai make you more productive", "ai productivity survey 2026" ], "signal": [ { "source": "Launches", "text": "6 model launches from 4 labs on 23 Sept 2026, including Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS." }, { "source": "Arena", "text": "claude-opus-4-6-high leads the Arena coding board on the 13 Sept 2026 snapshot (Anthropic)." } ], "read": [ "New models arrive every week, and each launch promises productivity. Anthropic's Economic Index asked Claude users what actually changed for them. The question here: when AI helps, what kind of help is it?", "It is mostly speed and reach. **86%** say they work faster and 82% say they can now do things they could not do before. 69% report better quality. Only **27%** report cost savings, and 10% rate their own job loss as likely.", "That shapes how AI products should sell. A buyer who feels faster wants to do more, not spend less. A pitch built on cutting cost runs into the smallest number on this chart. A pitch built on what a team can now take on matches what users already report. Price it on the extra output, and sell it where the extra work has a buyer." ], "sources": [ { "name": "Anthropic Economic Index, 2026 survey report", "url": "https://www.anthropic.com/research/economic-index-june-2026-report" } ], "data": { "type": "hbar", "fmt": "pct", "labelHead": "Reported effect", "valueHead": "Share of users", "rows": [ { "label": "Gain: speed", "v": 86 }, { "label": "Gain: scope", "v": 82 }, { "label": "Gain: quality", "v": 69 }, { "label": "Learn more with AI", "v": 68 }, { "label": "AI increased market value of my skills", "v": 57 }, { "label": "Gain: cost savings", "v": 27 }, { "label": "Rated own job loss likely/very likely", "v": 10 } ] } }, { "date": "2026-09-22", "url": "https://modevc.com/charts/ollama-downloads-local-llm-adoption/", "markdown": "https://modevc.com/charts/ollama-downloads-local-llm-adoption/index.md", "title": "Ollama Downloads: Is Local LLM Adoption Keeping Up?", "dek": "Ollama's JavaScript client grew 2.1×, but the cloud SDKs grew 8.7×. Local AI is growing; it is not where most products are built.", "claim": "On npm, Ollama's client fell from 27 to 6.6 downloads per 1,000 cloud lab SDK downloads in 46 weeks. On PyPI, after its counting change, it is 21.5.", "sub": "Weekly downloads of the ollama client per 1,000 combined downloads of the OpenAI, Anthropic and Google SDKs. npm: complete weeks, 6 Oct 2025 – 30 Aug 2026. PyPI: complete weeks after the 25 Aug 2026 counting change only.", "note": "The ollama package is one client for a local model server. Apps can also reach local models through other clients, and local tools that are not on npm or PyPI are not counted, so this is a lower bound on local use. Downloads are installs (including CI), not users. PyPI changed how it counts on 25 Aug 2026; we use only whole weeks after that date and only ratios, never PyPI levels before and after. Weeks with an npm gap day are dropped.", "take": "Local models win on privacy. The cloud wins on distribution, and it is pulling away.", "back": "Companies that package open-weight models for buyers who must keep data in-house, sold through channels those buyers already use.", "keywords": [ "ollama downloads", "local llm adoption", "ollama vs openai api" ], "signal": [ { "source": "Launches", "text": "Of the 44 model launches in our feed from 6 Jul to 22 Sep, 14 were closed-weight, 1 was confirmed open-weight and 29 were unclear when we checked." }, { "source": "npm", "text": "ollama had 0.67M npm downloads in the week of 24 Aug, 2.1× the week of 6 Oct 2025. The three cloud SDKs grew 8.7×." }, { "source": "PyPI", "text": "In the week of 14 Sep, Python developers installed ollama 21.5 times per 1,000 cloud SDK installs, 3.3× the npm rate." } ], "read": [ "Our launch feed confirms open weights for 1 of the 44 models launched since 6 July; 14 are closed and the rest are unconfirmed. Open weights are what local AI runs on. So how much are developers building on local models compared with cloud APIs?", "Less and less, relative to the cloud. On npm, Ollama's client grew **2.1×**, but the OpenAI, Anthropic and Google SDKs together grew **8.7×**. Its rate fell from 27 to **6.6** installs per 1,000 cloud SDK installs, 4.1× lower. Python is different: in the week of 14 Sep, after PyPI's counting change, the rate there was **21.5**, 3.3× the latest JavaScript rate.", "JavaScript is where products ship to users, and there the cloud is winning by a wide and widening margin. Local models show up more in Python, the language of experiments, fine-tuning and data jobs. For founders, local AI is not a mass developer market. It is a sales motion: buyers who cannot send data to a cloud API, reached through the vendors and IT channels they already trust." ], "sources": [ { "name": "npm registry downloads API", "url": "https://api.npmjs.org/downloads/range/2025-10-01:2026-10-06/ollama" }, { "name": "pypistats.org API (post-25 Aug 2026 only)", "url": "https://pypistats.org/api/packages/ollama/overall" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://www.anthropic.com/claude-opus-5-5" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "mon", "endLabels": true, "matchMs": 259200000, "series": [ { "name": "npm: ollama per 1,000 lab SDK downloads", "short": "npm", "pts": [ { "t": 1759708800000, "y": 27.2, "note": "0.32M ollama" }, { "t": 1760313600000, "y": 25.8, "note": "0.31M ollama" }, { "t": 1760918400000, "y": 27.1, "note": "0.32M ollama" }, { "t": 1761523200000, "y": 25.7, "note": "0.30M ollama" }, { "t": 1762128000000, "y": 23.5, "note": "0.29M ollama" }, { "t": 1762732800000, "y": 25.3, "note": "0.31M ollama" }, { "t": 1763337600000, "y": 32.4, "note": "0.43M ollama" }, { "t": 1763942400000, "y": 37.3, "note": "0.45M ollama" }, { "t": 1764547200000, "y": 36.8, "note": "0.53M ollama" }, { "t": 1765152000000, "y": 27.7, "note": "0.40M ollama" }, { "t": 1765756800000, "y": 20.3, "note": "0.29M ollama" }, { "t": 1766361600000, "y": 24.6, "note": "0.23M ollama" }, { "t": 1766966400000, "y": 24.9, "note": "0.24M ollama" }, { "t": 1767571200000, "y": 21.9, "note": "0.34M ollama" }, { "t": 1768176000000, "y": 20.8, "note": "0.35M ollama" }, { "t": 1768780800000, "y": 24.1, "note": "0.44M ollama" }, { "t": 1769385600000, "y": 27.6, "note": "0.60M ollama" }, { "t": 1769990400000, "y": 29.5, "note": "0.67M ollama" }, { "t": 1770595200000, "y": 24.8, "note": "0.59M ollama" }, { "t": 1771200000000, "y": 21, "note": "0.49M ollama" }, { "t": 1771804800000, "y": 14.9, "note": "0.43M ollama" }, { "t": 1772409600000, "y": 14.6, "note": "0.46M ollama" }, { "t": 1773014400000, "y": 13.5, "note": "0.48M ollama" }, { "t": 1773619200000, "y": 12.4, "note": "0.43M ollama" }, { "t": 1774224000000, "y": 13.1, "note": "0.47M ollama" }, { "t": 1774828800000, "y": 11.9, "note": "0.44M ollama" }, { "t": 1775433600000, "y": 11.4, "note": "0.46M ollama" }, { "t": 1776038400000, "y": 10.7, "note": "0.46M ollama" }, { "t": 1776643200000, "y": 10.4, "note": "0.47M ollama" }, { "t": 1777248000000, "y": 9.9, "note": "0.50M ollama" }, { "t": 1777852800000, "y": 11.6, "note": "0.56M ollama" }, { "t": 1778457600000, "y": 12, "note": "0.61M ollama" }, { "t": 1779062400000, "y": 10.7, "note": "0.58M ollama" }, { "t": 1779667200000, "y": 8.5, "note": "0.52M ollama" }, { "t": 1780876800000, "y": 8.3, "note": "0.54M ollama" }, { "t": 1781481600000, "y": 7.9, "note": "0.55M ollama" }, { "t": 1782691200000, "y": 8.1, "note": "0.52M ollama" }, { "t": 1783900800000, "y": 9, "note": "0.65M ollama" }, { "t": 1784505600000, "y": 8.7, "note": "0.65M ollama" }, { "t": 1785110400000, "y": 8.9, "note": "0.71M ollama" }, { "t": 1785715200000, "y": 8.3, "note": "0.66M ollama" }, { "t": 1786924800000, "y": 7, "note": "0.63M ollama" }, { "t": 1787529600000, "y": 6.6, "note": "0.67M ollama" } ] }, { "name": "PyPI: ollama per 1,000 lab SDK downloads", "short": "PyPI", "dots": true, "pts": [ { "t": 1788134400000, "y": 22.1, "note": "2.8M ollama" }, { "t": 1788739200000, "y": 21.8, "note": "2.8M ollama" }, { "t": 1789344000000, "y": 21.5, "note": "2.9M ollama" } ] } ], "ann": [ { "t": 1763942400000, "y": 37.3088985343783, "text": "npm peak 37.3 (24 Nov)", "dy": -12 } ] } }, { "date": "2026-09-21", "url": "https://modevc.com/charts/langchain-alternatives-downloads-share/", "markdown": "https://modevc.com/charts/langchain-alternatives-downloads-share/index.md", "title": "LangChain Alternatives: Downloads vs. Newer AI SDKs", "dek": "LangChain's JavaScript core grew 3.2× in 46 weeks, yet its share of framework installs fell from 32% to 19%. An early default can grow and still fade.", "claim": "LangChain's share of weekly npm framework downloads fell from 32% to 19% in 46 weeks, while its own downloads grew 3.2×.", "sub": "Share of combined weekly npm downloads of @langchain/core, ai (Vercel AI SDK) and @openai/agents, week of 6 Oct 2025 vs week of 24 Aug 2026, in %.", "note": "We use @langchain/core for LangChain because every LangChain.js app installs it; the langchain package itself had 0.67 downloads per core download in the first week and 0.53 in the last, so adding it would double-count. Shares are of these three packages only. The OpenAI Agents SDK starts from a small base (0.11M a week), so its 15.0× growth says more about its youth than its size. Weeks with an npm gap day are dropped; the latest complete week ends 30 Aug. PyPI figures use only data after the 25 Aug 2026 counting change.", "take": "Growing 3.2× while losing share is how a default fades.", "back": "Developer tools that win the default for new projects in a fast-growing category, judged by share, not totals.", "keywords": [ "langchain alternatives", "langchain downloads", "langchain vs vercel ai sdk" ], "signal": [ { "source": "npm", "text": "@langchain/core had 5.7M downloads in the week of 24 Aug, 3.2× the week of 6 Oct 2025. The Vercel AI SDK had 23.6M." }, { "source": "npm", "text": "The OpenAI Agents SDK grew from 0.11M to 1.6M weekly downloads, taking 5.2% of the three." }, { "source": "PyPI", "text": "In Python, after the 25 Aug counting change, langchain moved +3.6% between the weeks of 31 Aug and 14 Sep; openai moved +7.8%." } ], "read": [ "LangChain's npm downloads are still rising. But JavaScript developers now have other places to start: Vercel's AI SDK, and OpenAI's own Agents SDK. Is LangChain keeping its place?", "No. @langchain/core grew **3.2×** in 46 weeks, to 5.7M a week. But the Vercel AI SDK grew 6.5× and the OpenAI Agents SDK 15.0× from a small base. LangChain's share of the three fell from **32%** to **19%**. Python shows the same drift on a short, post-break window: langchain +3.6% in two weeks, openai +7.8%.", "This is how an early default loses its distribution. Existing projects keep installing it, so the total rises and the dashboard looks fine. New projects pick something else, and that only shows in share. For founders, the number that predicts the next year is share of new projects. For incumbents, a growing total is not proof of a moat." ], "sources": [ { "name": "npm registry downloads API", "url": "https://api.npmjs.org/downloads/range/2025-10-01:2026-10-06/@langchain/core" }, { "name": "pypistats.org API (post-25 Aug 2026 only)", "url": "https://pypistats.org/api/packages/langchain/overall" } ], "data": { "type": "slope", "fmt": "pct", "max": 100, "cols": [ "6 Oct 2025", "24 Aug 2026" ], "colSub": [ "share of weekly downloads", "share of weekly downloads" ], "rows": [ { "label": "LangChain", "a": 32.1, "b": 18.5 }, { "label": "Vercel AI SDK", "a": 66, "b": 76.3 }, { "label": "OpenAI Agents SDK", "a": 1.9, "b": 5.2, "logo": "lh-openai" } ] } }, { "date": "2026-09-20", "url": "https://modevc.com/charts/mcp-adoption-model-context-protocol-downloads/", "markdown": "https://modevc.com/charts/mcp-adoption-model-context-protocol-downloads/index.md", "title": "MCP Adoption: Model Context Protocol SDK Downloads", "dek": "The MCP SDK is the most downloaded AI package on npm in our set, 1.3× the OpenAI SDK. A protocol spreads through other people's products.", "claim": "The MCP SDK had 52.3M npm downloads in the week of 24 Aug, 1.3× the OpenAI SDK and more than any of the 12 other AI packages we track.", "sub": "npm downloads in the week of 24 Aug 2026 – 30 Aug (Monday–Sunday), 13 AI developer packages.", "note": "Downloads count installs, including CI and every package that depends on these SDKs; the MCP SDK is pulled in by many agents, editors and servers, which is the point of this chart, not a flaw in it. It is the latest week with no npm gap day (the API returns 0 on some days) for all 13 packages. PyPI changed how it counts on 25 Aug 2026, so the PyPI ratio uses only 13 Sep–19 Sep, after the change.", "take": "The widest distribution is being a dependency of everyone else.", "back": "Companies that become the default MCP server for a system of record agents cannot avoid.", "keywords": [ "mcp adoption", "model context protocol downloads", "mcp sdk npm" ], "signal": [ { "source": "npm", "text": "@modelcontextprotocol/sdk passed 50 million weekly downloads for the first time in the week of 27 Jul 2026 (53.3M)." }, { "source": "PyPI", "text": "On PyPI, the mcp package had 80 downloads for every 100 of openai in the 7 days to 19 Sep (54.1M vs 67.7M), all after the 25 Aug counting change." }, { "source": "npm", "text": "In the week of 6 Oct 2025 the MCP SDK was already level with the OpenAI SDK: 7.6M vs 7.3M." } ], "read": [ "The Model Context Protocol is how an AI agent connects to a tool or a data source. Its SDK first passed 50 million weekly npm downloads in the week of 27 Jul. The model SDKs get the attention. How does a protocol compare with them?", "It is bigger than all of them. In the week of 24 Aug the MCP SDK had **52.3M** downloads, 1.3× the next biggest, the OpenAI SDK (39.7M). In the week of 6 Oct 2025 the two were level (1.05×); since then MCP grew **6.9×**. Python tells the same story in a different ratio: after PyPI's counting change, mcp had 80 downloads for every 100 of openai.", "A protocol SDK does not need a sales team. Every agent, editor and server that speaks MCP installs it, so it rides on other companies' distribution. For founders, the lesson is to be on the other end of that protocol: the default MCP server for a system of record gets called by every agent that needs that system, whichever model sits behind it." ], "sources": [ { "name": "npm registry downloads API", "url": "https://api.npmjs.org/downloads/range/2025-10-01:2026-10-06/@modelcontextprotocol/sdk" }, { "name": "pypistats.org API (post-25 Aug 2026 only)", "url": "https://pypistats.org/api/packages/mcp/overall" } ], "data": { "type": "hbar", "fmt": "si", "labelHead": "Package", "valueHead": "Downloads, week of 24 Aug", "rows": [ { "label": "MCP SDK", "v": 52304301 }, { "label": "OpenAI SDK", "v": 39740141, "logo": "lh-openai" }, { "label": "Anthropic SDK", "v": 38688134, "logo": "si-anthropic" }, { "label": "Vercel AI SDK (ai)", "v": 23643538 }, { "label": "Google Gen AI SDK", "v": 23365829, "logo": "si-google" }, { "label": "@ai-sdk/anthropic", "v": 11916699 }, { "label": "@ai-sdk/openai", "v": 11903069 }, { "label": "Mistral SDK", "v": 8356518, "logo": "si-mistralai" }, { "label": "@langchain/core", "v": 5724084 }, { "label": "langchain", "v": 3042925 }, { "label": "OpenAI Agents SDK", "v": 1601128, "logo": "lh-openai" }, { "label": "groq-sdk", "v": 946186 }, { "label": "ollama", "v": 672102 } ] } }, { "date": "2026-09-19", "url": "https://modevc.com/charts/vercel-ai-sdk-downloads-vs-lab-sdks/", "markdown": "https://modevc.com/charts/vercel-ai-sdk-downloads-vs-lab-sdks/index.md", "title": "Vercel AI SDK Downloads vs. the Lab SDKs", "dek": "Vercel's AI SDK grew 6.5× in under a year, but the lab SDKs grew 8.7×. The layer that makes models swappable did not capture the growth.", "claim": "The Vercel AI SDK fell from 31 to 23 weekly downloads per 100 downloads of the OpenAI, Anthropic and Google SDKs, after a peak of 40.", "sub": "Weekly npm downloads of the ai package per 100 combined downloads of openai, @anthropic-ai/sdk and @google/genai. Complete Monday–Sunday weeks, 6 Oct 2025 – 30 Aug 2026.", "note": "ai is the core package of Vercel's AI SDK; model providers plug in through adapter packages such as @ai-sdk/openai. Downloads count installs, including CI and packages that depend on these SDKs, so lab SDK counts include other tools built on top of them. Weeks with an npm gap day (the API returns 0) are dropped for all packages; the latest complete week is 24 Aug.", "take": "Making models swappable is a feature. Owning what cannot be swapped is the business.", "back": "Developer platforms that pair a model-neutral layer with something sticky: hosting, data, evals or billing.", "keywords": [ "vercel ai sdk downloads", "vercel ai sdk vs openai sdk", "ai sdk npm" ], "signal": [ { "source": "Launches", "text": "10 model launches from 8 labs in our launch feed between 1 and 19 September; every one is a reason to swap models." }, { "source": "npm", "text": "Inside the AI SDK, the Anthropic adapter went from 61% to 100% of the OpenAI adapter's weekly downloads (11.9M vs 11.9M, week of 24 Aug)." }, { "source": "npm", "text": "ai had 23.6M downloads in the week of 24 Aug, up from 3.6M in the week of 6 Oct 2025." } ], "read": [ "Our launch feed lists 10 new models from 8 labs in the first 19 days of September. A layer above the models, such as Vercel's AI SDK, promises that a team can try each one by changing a line of config. If models really are swappable, that layer should be taking a growing share of developer installs. Is it?", "Not on npm. The ai package grew **6.5×**, from 3.6M to 23.6M weekly downloads. The three lab SDKs together grew **8.7×**. Per 100 lab SDK downloads, ai went from 31 to a peak of 40 in the week of 24 Nov 2025, then down to 23. Swapping does happen inside the layer: the Anthropic adapter went from 61% to 100% of the OpenAI adapter.", "So the abstraction works, and it still did not take the market. Most new installs went straight to a lab. A neutral layer makes the model a commodity, which is our thesis, but making something a commodity does not mean you own the customer. The switch is useful. The moat is whatever the developer cannot move as easily: their deployment, their data, their evals, their bill." ], "sources": [ { "name": "npm registry downloads API", "url": "https://api.npmjs.org/downloads/range/2025-10-01:2026-10-06/ai" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://openai.com/index/gpt-6-astra/" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "mon", "yDomain": [ 0, 60 ], "matchMs": 259200000, "series": [ { "name": "ai per 100 lab SDK downloads", "short": "ai", "pts": [ { "t": 1759708800000, "y": 31.1, "note": "3.6M ai vs 11.7M lab SDKs" }, { "t": 1760313600000, "y": 30.8, "note": "3.7M ai vs 12.2M lab SDKs" }, { "t": 1760918400000, "y": 34.1, "note": "4.0M ai vs 11.7M lab SDKs" }, { "t": 1761523200000, "y": 36.4, "note": "4.2M ai vs 11.6M lab SDKs" }, { "t": 1762128000000, "y": 37.5, "note": "4.6M ai vs 12.2M lab SDKs" }, { "t": 1762732800000, "y": 39.2, "note": "4.8M ai vs 12.3M lab SDKs" }, { "t": 1763337600000, "y": 38.5, "note": "5.1M ai vs 13.3M lab SDKs" }, { "t": 1763942400000, "y": 40.4, "note": "4.9M ai vs 12.1M lab SDKs" }, { "t": 1764547200000, "y": 38, "note": "5.5M ai vs 14.4M lab SDKs" }, { "t": 1765152000000, "y": 35.9, "note": "5.2M ai vs 14.5M lab SDKs" }, { "t": 1765756800000, "y": 36.5, "note": "5.3M ai vs 14.4M lab SDKs" }, { "t": 1766361600000, "y": 32.4, "note": "3.1M ai vs 9.4M lab SDKs" }, { "t": 1766966400000, "y": 29.6, "note": "2.8M ai vs 9.6M lab SDKs" }, { "t": 1767571200000, "y": 35.5, "note": "5.6M ai vs 15.7M lab SDKs" }, { "t": 1768176000000, "y": 37, "note": "6.2M ai vs 16.7M lab SDKs" }, { "t": 1768780800000, "y": 35.1, "note": "6.4M ai vs 18.2M lab SDKs" }, { "t": 1769385600000, "y": 32.3, "note": "7.0M ai vs 21.8M lab SDKs" }, { "t": 1769990400000, "y": 33.2, "note": "7.6M ai vs 22.8M lab SDKs" }, { "t": 1770595200000, "y": 32.6, "note": "7.8M ai vs 23.9M lab SDKs" }, { "t": 1771200000000, "y": 34, "note": "8.0M ai vs 23.4M lab SDKs" }, { "t": 1771804800000, "y": 30.2, "note": "8.7M ai vs 28.8M lab SDKs" }, { "t": 1772409600000, "y": 31.5, "note": "9.9M ai vs 31.4M lab SDKs" }, { "t": 1773014400000, "y": 28.5, "note": "10.1M ai vs 35.4M lab SDKs" }, { "t": 1773619200000, "y": 28.1, "note": "9.8M ai vs 34.9M lab SDKs" }, { "t": 1774224000000, "y": 29.1, "note": "10.4M ai vs 35.9M lab SDKs" }, { "t": 1774828800000, "y": 27.2, "note": "10.1M ai vs 37.1M lab SDKs" }, { "t": 1775433600000, "y": 26.5, "note": "10.6M ai vs 40.1M lab SDKs" }, { "t": 1776038400000, "y": 26.9, "note": "11.5M ai vs 42.9M lab SDKs" }, { "t": 1776643200000, "y": 26.2, "note": "11.9M ai vs 45.3M lab SDKs" }, { "t": 1777248000000, "y": 23.9, "note": "12.1M ai vs 50.7M lab SDKs" }, { "t": 1777852800000, "y": 26.2, "note": "12.6M ai vs 48.0M lab SDKs" }, { "t": 1778457600000, "y": 26.3, "note": "13.2M ai vs 50.4M lab SDKs" }, { "t": 1779062400000, "y": 25.5, "note": "13.7M ai vs 54.0M lab SDKs" }, { "t": 1779667200000, "y": 21, "note": "13.0M ai vs 61.9M lab SDKs" }, { "t": 1780876800000, "y": 23.2, "note": "15.1M ai vs 65.2M lab SDKs" }, { "t": 1781481600000, "y": 22.9, "note": "15.7M ai vs 68.8M lab SDKs" }, { "t": 1782691200000, "y": 23.8, "note": "15.1M ai vs 63.7M lab SDKs" }, { "t": 1783900800000, "y": 24.4, "note": "17.7M ai vs 72.7M lab SDKs" }, { "t": 1784505600000, "y": 25.1, "note": "18.8M ai vs 74.7M lab SDKs" }, { "t": 1785110400000, "y": 24.7, "note": "19.8M ai vs 80.2M lab SDKs" }, { "t": 1785715200000, "y": 25.9, "note": "20.6M ai vs 79.3M lab SDKs" }, { "t": 1786924800000, "y": 25, "note": "22.4M ai vs 89.6M lab SDKs" }, { "t": 1787529600000, "y": 23.2, "note": "23.6M ai vs 101.8M lab SDKs" } ] } ], "ann": [ { "t": 1763942400000, "y": 40.383319751280624, "text": "peak 40 (24 Nov)", "dy": -12 }, { "t": 1787529600000, "y": 23.226824610588448, "text": "23", "dy": -12 } ] } }, { "date": "2026-09-18", "url": "https://modevc.com/charts/anthropic-sdk-vs-openai-sdk-npm-downloads/", "markdown": "https://modevc.com/charts/anthropic-sdk-vs-openai-sdk-npm-downloads/index.md", "title": "Anthropic SDK vs. OpenAI SDK: npm Downloads", "dek": "In 46 weeks the Anthropic SDK went from 34% to 97% of the OpenAI SDK's npm downloads. Developer distribution can be won late, and lost fast.", "claim": "Anthropic's SDK went from 34% to 97% of the OpenAI SDK's weekly npm downloads in 46 weeks. It passed OpenAI in one week (25 May).", "sub": "Weekly npm downloads of the official JavaScript SDKs (@anthropic-ai/sdk, openai, @google/genai), complete Monday–Sunday weeks, 6 Oct 2025 – 30 Aug 2026.", "note": "Downloads count installs, including CI runs, mirrors and packages that depend on an SDK; they are not people or API spend. The npm API returns 0 on some days; we treat those as gaps and drop any week with a gap. That is why the line stops at the week of 24 Aug: the API returned 0 on 3 Sep, 7 Sep, 8 Sep and 15 Sep. Week-to-week moves of a few points are noise.", "take": "Developer defaults do not drift. They flip, often inside one quarter.", "back": "Developer tools that are taking the default from a category leader, visible week by week in install share.", "keywords": [ "anthropic sdk downloads", "openai npm downloads", "anthropic vs openai api usage" ], "signal": [ { "source": "Launches", "text": "All three labs shipped in early September: [Claude Fable 5.1 and Mythos 5.1](https://www.anthropic.com/claude-fable-and-mythos-5-1) (1 Sep), Gemini 3.8 Flash (2 Sep) and [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) (3 Sep)." }, { "source": "npm", "text": "@anthropic-ai/sdk had 38.7M downloads in the week of 24 Aug, against 39.7M for openai." }, { "source": "npm", "text": "The biggest eight-week move: from 51% of OpenAI (week of 2 Mar) to 87% (week of 27 Apr)." } ], "read": [ "In the first days of September, Anthropic, Google and OpenAI each shipped new models. For a JavaScript developer, the first step with any of them is the same: install the lab's SDK. So whose SDK do developers install?", "In the week of 6 Oct 2025 the Anthropic SDK had **34%** of the OpenAI SDK's npm downloads. In the week of 24 Aug it had **97%**, and in the week of 25 May it was ahead. Anthropic grew 15.5× in that time, OpenAI 5.5× and Google 12.2×. The catch-up was not smooth: 57% of it came in eight weeks, from 2 Mar to 27 Apr.", "Developer defaults look locked until they move, and then they move in one quarter. That cuts both ways for founders who sell to developers. A second-place tool can take the default with a burst of better product and presence where developers start a project. A leader can lose it just as fast. Track your installs as a share of the leader's, every week; your own total can rise while the default slips away." ], "sources": [ { "name": "npm registry downloads API", "url": "https://api.npmjs.org/downloads/range/2025-10-01:2026-10-06/@anthropic-ai/sdk" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://openai.com/index/gpt-6-astra/" } ], "data": { "type": "line", "yFmt": "si", "xFmt": "mon", "endLabels": true, "matchMs": 259200000, "series": [ { "name": "Anthropic SDK", "short": "Anthropic", "logo": "si-anthropic", "pts": [ { "t": 1759708800000, "y": 2499103, "note": "34% of OpenAI" }, { "t": 1760313600000, "y": 2541053, "note": "34% of OpenAI" }, { "t": 1760918400000, "y": 2700081, "note": "36% of OpenAI" }, { "t": 1761523200000, "y": 2631665, "note": "36% of OpenAI" }, { "t": 1762128000000, "y": 2741103, "note": "36% of OpenAI" }, { "t": 1762732800000, "y": 2833461, "note": "37% of OpenAI" }, { "t": 1763337600000, "y": 3144498, "note": "39% 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9778096 }, { "t": 1773619200000, "y": 9349700 }, { "t": 1774224000000, "y": 9127210 }, { "t": 1774828800000, "y": 9066021 }, { "t": 1775433600000, "y": 9575222 }, { "t": 1776038400000, "y": 10161823 }, { "t": 1776643200000, "y": 10582090 }, { "t": 1777248000000, "y": 11243349 }, { "t": 1777852800000, "y": 10400075 }, { "t": 1778457600000, "y": 10900111 }, { "t": 1779062400000, "y": 11906967 }, { "t": 1779667200000, "y": 13542565 }, { "t": 1780876800000, "y": 15897950 }, { "t": 1781481600000, "y": 16986707 }, { "t": 1782691200000, "y": 15279809 }, { "t": 1783900800000, "y": 16157151 }, { "t": 1784505600000, "y": 16448511 }, { "t": 1785110400000, "y": 17760454 }, { "t": 1785715200000, "y": 17455774 }, { "t": 1786924800000, "y": 21190403 }, { "t": 1787529600000, "y": 23365829 } ] } ], "ann": [ { "t": 1779667200000, "y": 24393429, "text": "Anthropic passes OpenAI (25 May)", "dy": -14 } ] } }, { "date": "2026-09-17", "url": "https://modevc.com/charts/llm-rankings-by-category-specialists/", "markdown": "https://modevc.com/charts/llm-rankings-by-category-specialists/index.md", "title": "LLM Rankings by Category: The Hidden Specialists", "dek": "22 of the Arena's top 60 models rank at least 10 places higher in one category than overall. The overall list hides specialists.", "claim": "claude-opus-4-5-20251101-high-32k is #49 overall but #14 in coding. gpt-5.6-terra-xhigh is #52 overall but #24 in expert prompts.", "sub": "Places gained in the model's strongest Arena category vs its overall rank, top 60 overall models, 13 Sept 2026. One bar per model; top 14 shown.", "note": "Categories: coding, math, creative writing, hard prompts, instruction following, longer queries, expert. Arena category boards re-score the same blind votes, filtered by prompt type. For each category we rank the overall and category boards over the models present on both, so a smaller board does not move ranks by itself. Only category ratings with at least 1,000 votes count. Neighbouring ranks often overlap within confidence intervals (±5–14 points here), so read gains under about 10 places as noise.", "take": "Overall rank picks a model for everyone. Your customers are not everyone.", "back": "Vertical products that score models on their own customers' tasks and keep that test set as an asset.", "keywords": [ "llm leaderboard", "llm rankings by category", "best llm for hard prompts" ], "signal": [ { "source": "Launches", "text": "GPT-5.6 Sol, Luna and Terra (9 Jul 2026). On 13 Sept 2026, 4 OpenAI models in the overall top 60 rank 10+ places higher on expert prompts." }, { "source": "Arena", "text": "Best LLM for hard prompts on 13 Sept 2026: claude-opus-4-6-high (Anthropic), #4 overall." }, { "source": "Arena", "text": "Models that gain 10+ places, by their strongest category: expert 6, coding 5, creative writing 5, longer queries 3, math 1, hard prompts 1, instruction following 1." } ], "read": [ "Most leaderboard readers stop at the overall list. But the Arena also scores each model on categories of prompts: coding, math, expert questions, long queries and more. Is the overall rank a good guide to which model fits a given job?", "Often it is not. **22** of the top 60 models rank at least 10 places higher in one category. Anthropic's claude-opus-4-5-20251101-high-32k, a release dated Nov 2025 in its name, is #49 overall but **#14 in coding**. OpenAI's gpt-5.6-terra-xhigh is #52 overall and **#24 on expert prompts**. Each of these gains rests on at least 1,000 category votes.", "The overall rank is an average over everyone's prompts. A vertical product serves one kind of prompt, so it should test on its own work, not on the average. Teams that test this way can pick a model that most buyers overlook. The work of testing also builds a set of real customer tasks that no competitor has." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "hbar", "fmt": "num", "labelHead": "Model · category", "valueHead": "Places higher than overall", "rows": [ { "label": "claude-opus-4-5-20251101-high-32k · coding", "v": 35, "logo": "si-anthropic" }, { "label": "gpt-5.6-terra-xhigh · expert", "v": 28, "logo": "lh-openai" }, { "label": "claude-opus-4-5-20251101 · coding", "v": 25, "logo": "si-anthropic" }, { "label": "gpt-5.6-sol-xhigh · expert", "v": 23, "logo": "lh-openai" }, { "label": "claude-sonnet-4-6 · coding", "v": 22, "logo": "si-anthropic" }, { "label": "claude-opus-4-8 · longer queries", "v": 18, "logo": "si-anthropic" }, { "label": "mimo-v2.5-pro · coding", "v": 16, "logo": "si-xiaomi" }, { "label": "kimi-k2.5-thinking · math", "v": 15, "logo": "lh-moonshot" }, { "label": "claude-opus-4-8-high · longer queries", "v": 14, "logo": "si-anthropic" }, { "label": "deepseek-v4-pro-high-preview · creative writing", "v": 14, "logo": "si-deepseek" }, { "label": "glm-5.3-flash · coding", "v": 13, "logo": "lh-zai" }, { "label": "gpt-5.5 · expert", "v": 13, "logo": "lh-openai" }, { "label": "gemini-2.5-pro · creative writing", "v": 13, "logo": "si-google" }, { "label": "kimi-k3-max · expert", "v": 12, "logo": "lh-moonshot" } ] } }, { "date": "2026-09-16", "url": "https://modevc.com/charts/best-ai-for-creative-writing/", "markdown": "https://modevc.com/charts/best-ai-for-creative-writing/index.md", "title": "Best AI for Creative Writing: Arena Rankings", "dek": "claude-fable-5.1-max leads Arena creative writing, with 9 more models inside its interval. Some models that rank high overall fall a long way here.", "claim": "Anthropic has 7 of the top 10 creative writing models and Google 3. Meta's muse-spark-1.2 (xHigh) is #11 overall but #31 in creative writing.", "sub": "Top 15 models in the Arena creative writing category, 13 Sept 2026. Bar = Arena points above the #50 creative writing model (qwen3.6-max-preview, 1435). Overall rank in the table and tooltips.", "note": "Arena creative writing is a category of the Arena text board: blind human votes on prompts that ask for stories, poems, scripts and similar work. It measures reader preference, not originality or sales. Ranks in the table and tooltips are recomputed over the 400 models present on both boards, so the two ranks compare like with like. 10 models have a 95% interval that reaches the leader's lower bound (1490). Latest snapshots on or before 16 Sept 2026.", "take": "A model can copy a voice. It cannot copy a writer's readers.", "back": "Writing tools that sit where writers already publish and get paid, not another blank text box.", "keywords": [ "best ai for creative writing", "best ai for writing stories", "ai model comparison" ], "signal": [ { "source": "Launches", "text": "[Claude Fable 5.1](https://www.anthropic.com/claude-fable-and-mythos-5-1) (1 Sept 2026): claude-fable-5.1-max is #1 in creative writing on 1,233 votes." }, { "source": "Launches", "text": "Muse Spark 1.3 (2 Sept 2026): muse-spark-1.3-max is #10 overall and #25 in creative writing." }, { "source": "Arena", "text": "OpenAI's best creative writing model ranks #22; its best overall model ranks #26." } ], "read": [ "Anthropic released Claude Fable 5.1 on 1 Sept 2026, and Meta released Muse Spark 1.3 the next day. On the overall board they now rank #1 and #10. Creative writing is a separate test, so which AI is best for creative writing?", "**claude-fable-5.1-max** leads, but 9 other models are inside its interval, so the top is a group, not a single winner. Anthropic holds **7** of the top 10 and Google 3. The overall board does not predict this list well for every lab. Meta has 3 models in the overall top 15 that rank at least 10 places lower in creative writing; muse-spark-1.2 (xHigh) drops from #11 to **#31**.", "Writing is where a model's voice is part of the product. Readers notice it, and they prefer some voices to others. But a voice can be copied by the next release, and the 10 leaders here are within 27 points of each other. What a writing tool can keep is the writer: the drafts, the style guide, and the readers who come back for the next chapter." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Anthropic: Claude Fable and Mythos 5.1", "url": "https://www.anthropic.com/claude-fable-and-mythos-5-1" } ], "data": { "type": "hbar", "fmt": "pts", "labelHead": "Model", "valueHead": "Points above #50", "rows": [ { "label": "claude-fable-5.1-max", "v": 72.4, "logo": "si-anthropic" }, { "label": "claude-opus-4-6-high", "v": 69.5, "logo": "si-anthropic" }, { "label": "gemini-3.8-flash-high", "v": 62.9, "logo": "si-google" }, { "label": "claude-fable-5", "v": 61.1, "logo": "si-anthropic" }, { "label": "gemini-3.7-flash-high", "v": 56.3, "logo": "si-google" }, { "label": "claude-opus-5-max", "v": 56.2, "logo": "si-anthropic" }, { "label": "claude-opus-5-high", "v": 53, "logo": "si-anthropic" }, { "label": "claude-opus-4-7-high", "v": 49.8, "logo": "si-anthropic" }, { "label": "claude-opus-4-6", "v": 48.9, "logo": "si-anthropic" }, { "label": "gemini-3.1-pro-preview", "v": 46.3, "logo": "si-google" }, { "label": "qwen3.8-max", "v": 45.9, "logo": "si-qwen" }, { "label": "gemini-3-pro", "v": 45.4, "logo": "si-google" }, { "label": "claude-opus-4-7", "v": 42.9, "logo": "si-anthropic" }, { "label": "gemini-3.5-flash-high", "v": 33.2, "logo": "si-google" }, { "label": "gemini-3.5-flash-medium", "v": 32.3, "logo": "si-google" } ] } }, { "date": "2026-09-15", "url": "https://modevc.com/charts/best-ai-for-math/", "markdown": "https://modevc.com/charts/best-ai-for-math/index.md", "title": "Best AI for Math: The Arena Math Leaderboard", "dek": "claude-opus-5-max tops Arena math, but 14 models from 5 labs are statistically tied with it. 4 of them are lighter \"Flash\" models.", "claim": "14 models are statistically tied for #1 in Arena math, spanning 49 points. 4 are Flash models from Google and Z.ai.", "sub": "Top 18 models in the Arena math category, 13 Sept 2026. Bar = Arena points above the #50 math model (deepseek-v4-pro-high-preview, 1457).", "note": "Arena math is a category of the Arena text board: blind human votes on math prompts. Voters judge which answer they prefer; Arena does not check the answer against a key. \"Statistically tied\" means the model's 95% upper bound reaches the leader's lower bound (1518). \"Flash\" is taken from the model name; it marks a lab's lighter line, not a measured price. Latest snapshot on or before 15 Sept 2026.", "take": "14 models tie for the top in math. The tie goes to whoever already has the students.", "back": "Math and quantitative tools that reach users through a channel they already own, such as schools, exam prep or engineering teams.", "keywords": [ "best ai for math", "best llm for math", "llm benchmark" ], "signal": [ { "source": "Launches", "text": "Claude Opus 5 (24 Jul 2026): claude-opus-5-max leads math on 904 votes." }, { "source": "Launches", "text": "Gemini 3.7 Flash (13 Aug 2026) and [GLM-5.3-Flash](https://docs.z.ai/guides/vlm/glm-5.3-flash) (26 Aug 2026) both sit inside the leader's interval." }, { "source": "Arena", "text": "The median top-10 math model has 1,355 votes, against 25,382 on the overall board." } ], "read": [ "Google and Z.ai shipped lighter Flash models in August, and both now sit near the top of the Arena math category. So what is the best AI for math, and does the leaderboard know?", "**claude-opus-5-max** is #1 at 1537. But the math board has thin evidence: the median top-10 model has 1,355 votes and an interval of ±16 points, against ±5 on the overall board. As a result, **14 models** from Anthropic, Google, Z.ai, Alibaba and Meta cannot be separated from the leader. The best Flash model is #3. OpenAI's best math model is #16.", "When 14 models tie, a math product cannot win on the model. It wins on what surrounds the answer: the right next problem, the steps shown the way a teacher shows them, and access to the students and engineers who need it. A cheaper model that ties for first is a margin advantage, not a product." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Z.ai GLM-5.3-Flash release note", "url": "https://docs.z.ai/guides/vlm/glm-5.3-flash" } ], "data": { "type": "hbar", "fmt": "pts", "labelHead": "Model", "valueHead": "Points above #50", "rows": [ { "label": "claude-opus-5-max", "v": 79.8, "logo": "si-anthropic" }, { "label": "claude-opus-5-high", "v": 76.7, "logo": "si-anthropic" }, { "label": "gemini-3.7-flash-high", "v": 67.6, "logo": "si-google" }, { "label": "claude-fable-5.1-max", "v": 64.2, "logo": "si-anthropic" }, { "label": "claude-fable-5", "v": 64.2, "logo": "si-anthropic" }, { "label": "claude-opus-4-6-high", "v": 58.6, "logo": "si-anthropic" }, { "label": "claude-opus-4-6", "v": 54.7, "logo": "si-anthropic" }, { "label": "glm-5.3-flash", "v": 53.2, "logo": "lh-zai" }, { "label": "gemini-3.6-flash-high", "v": 48.6, "logo": "si-google" }, { "label": "gemini-3.5-flash-high", "v": 47.3, "logo": "si-google" }, { "label": "qwen3.8-max", "v": 43, "logo": "si-qwen" }, { "label": "glm-5.3-max", "v": 40.8, "logo": "lh-zai" }, { "label": "claude-opus-4-7-high", "v": 40.3, "logo": "si-anthropic" }, { "label": "kimi-k3-max", "v": 36.3, "logo": "lh-moonshot" }, { "label": "qwen3.7-max-preview", "v": 35.9, "logo": "si-qwen" }, { "label": "gpt-5.4-high", "v": 32.6, "logo": "lh-openai" }, { "label": "claude-opus-4-7", "v": 31.1, "logo": "si-anthropic" }, { "label": "muse-spark-1.3-max", "v": 31.1, "logo": "si-meta" } ] } }, { "date": "2026-09-14", "url": "https://modevc.com/charts/llm-leaderboard-top-10-gap/", "markdown": "https://modevc.com/charts/llm-leaderboard-top-10-gap/index.md", "title": "LLM Leaderboard Top 10: #1 Leads #10 by 18 Points", "dek": "The #10 model on the Arena board would now win 47% of blind votes against #1. When the top ten are this close, quality stops deciding the sale.", "claim": "The gap between Arena's #1 and #10 models fell from 148 points in Feb 2024 to 18. #10 now wins 47% of head-to-head votes against #1, up from 30%.", "sub": "Rating of the #1 model minus the #10 and #20 models, Arena text board (overall), Arena points, 9 Jan 2024 – 13 Sept 2026.", "note": "Ranks count every listed model, so effort variants of one model can fill several places: the 13 Sept 2026 top 10 holds models from only 3 labs. Counting each lab once, the gap from #1 to the fifth-best lab fell from 84 points (Jan 2024) to 32. Win rate uses Arena's scale: P(lower wins) = 1 / (1 + 10^(gap/400)).", "take": "When the top ten are tied, the buyer picks the vendor already on the invoice.", "back": "Companies that sell AI through a channel the buyer already pays: a cloud marketplace listing, an existing SaaS seat, an integrator.", "keywords": [ "llm leaderboard top 10", "llm leaderboard", "llm benchmark" ], "signal": [ { "source": "Launches", "text": "DeepSeek V4.1 Flash (10 Sep) and Kimi K2.8 Preview (11 Sep) were announced on consecutive days." }, { "source": "Arena", "text": "13 Sept 2026: muse-spark-1.3-max (Meta) entered the top 10 for the first time, at #10." }, { "source": "Arena", "text": "On the same snapshot, 6 models have a confidence interval that reaches claude-fable-5.1-max's lower bound, so they are statistically tied with #1." } ], "read": [ "In the days before this snapshot, two more labs shipped models: DeepSeek with DeepSeek V4.1 Flash and Moonshot with Kimi K2.8 Preview. On the 13 Sept 2026 Arena snapshot, claude-fable-5.1-max is #1 and muse-spark-1.3-max is #10. The question behind every \"LLM leaderboard top 10\" search is how much the ranking matters.", "Less and less. In Feb 2024 the #1 model led #10 by **148 points**; today the gap is **18**, and #1 to #20 is 32 (it was 188 in Feb 2024). At 18 points, #10 wins about 47% of blind votes against #1. On this snapshot 6 models are inside #1's confidence interval. Some of this is one lab's variants clustering, but counting each lab once the gap still fell from 84 to 32 points.", "When ten models are this close, a buyer picks on other things: price, speed, data rules, and which vendor is already on the purchase order. That last one is distribution. 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37, "note": "glm-5.1" }, { "t": 1785024000000, "y": 40, "note": "gemini-3-flash" }, { "t": 1785110400000, "y": 44, "note": "ernie-5.1" }, { "t": 1785369600000, "y": 44, "note": "ernie-5.1" }, { "t": 1785628800000, "y": 38, "note": "gpt-5.4-high" }, { "t": 1785715200000, "y": 40, "note": "ernie-5.1" }, { "t": 1785974400000, "y": 35, "note": "gpt-5.5-high" }, { "t": 1786320000000, "y": 38, "note": "gpt-5.5-high" }, { "t": 1786406400000, "y": 37, "note": "gpt-5.5-high" }, { "t": 1786492800000, "y": 37, "note": "gpt-5.5-high" }, { "t": 1787097600000, "y": 32, "note": "muse-spark" }, { "t": 1787270400000, "y": 31, "note": "muse-spark" }, { "t": 1787788800000, "y": 31, "note": "muse-spark" }, { "t": 1788220800000, "y": 31, "note": "muse-spark" }, { "t": 1788307200000, "y": 39, "note": "qwen3.7-max-preview" }, { "t": 1789084800000, "y": 36, "note": "qwen3.7-max-preview" }, { "t": 1789257600000, "y": 32, "note": "glm-5.3-max" } ] } ], "ann": [ { "t": 1706832000000, "y": 148, "text": "148 pts (Feb 2024)", "dy": -10 } ] } }, { "date": "2026-09-13", "url": "https://modevc.com/charts/chinese-ai-models-vs-us-models/", "markdown": "https://modevc.com/charts/chinese-ai-models-vs-us-models/index.md", "title": "Chinese AI Models vs. US Models: How Close Is China?", "dek": "The best Chinese model trails the best US model by 27 Arena points, down from 156 in Jan 2024. A year ago it was 27.", "claim": "qwen3.8-max trails claude-fable-5.1-max by 27 points. Over the past year the gap has stayed between 25 and 46; it was 156 in Jan 2024.", "sub": "Highest Arena text rating (overall) from a Chinese lab vs a US lab, last snapshot of each month, Jan 2024 – 13 Sept 2026.", "note": "Chinese labs = Alibaba (Qwen), DeepSeek, Z.ai, Moonshot, Xiaomi, MiniMax, Tencent, Baidu, ByteDance, StepFun, Meituan, Ant Group, 01.AI, InternLM. The other line is the best model from any non-Chinese lab; on every snapshot since Jan 2024 that model came from a US lab (OpenAI, Anthropic, Google or xAI), so we label it US. Ranking is by point rating; neighbouring models often overlap within confidence intervals. The dataset leaves out some experimental preview models in early 2025, which may overstate the brief Chinese lead. \"ChatGPT\" is a product; we compare OpenAI's best Arena model.", "take": "A 27-point gap does not decide the deal. Trust and procurement do.", "back": "Companies that sell AI into buyers who choose on provenance and compliance: regulated industries, the public sector, cross-border trade.", "keywords": [ "chinese ai models", "deepseek vs chatgpt", "ai model comparison" ], "signal": [ { "source": "Launches", "text": "DeepSeek V4.1 Flash (10 Sept 2026). Sources disagree on whether its weights are open." }, { "source": "Launches", "text": "Kimi K2.8 Preview (11 Sept 2026), reported by one tracker only." }, { "source": "Arena", "text": "qwen3.8-max (Alibaba, launched 2 Aug 2026) is the best Chinese model on 13 Sept 2026, #14 overall. 2 Chinese models are in the top 20." } ], "read": [ "DeepSeek and Moonshot both released new models in the week to 13 Sept 2026. That brings back an old question: are Chinese AI models catching up with US models?", "They closed most of the gap, then stopped. In Jan 2024 the best Chinese model was **156 points** behind. In Jan 2025 deepseek-r1 briefly led outright. Over the past year the gap has stayed between 25 and 46 points. Today it is **27**, which means the best Chinese model would win about 46% of blind votes. DeepSeek vs ChatGPT: deepseek-v4-pro ranks #43 and OpenAI's best, gpt-5.5-high, ranks #26.", "At 46% of votes, quality does not decide the choice between a Chinese and a US model. Buyers decide on other things: where the data is processed, which rules apply, and whom the buyer already trusts. Those are distribution questions. Each side's models sell most easily where its labs already have the cloud contracts and the compliance approvals." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "year", "endLabels": true, "matchMs": 1728000000, "series": [ { "name": "Best Chinese-lab model", "short": "China", "pts": [ { "t": 1706140800000, "y": 1034, "note": "qwen-14b-chat (Alibaba)" }, { "t": 1707955200000, "y": 1147, "note": "qwen1.5-72b-chat (Alibaba)" }, { "t": 1711497600000, "y": 1148, "note": "qwen1.5-72b-chat (Alibaba)" }, { "t": 1714089600000, "y": 1153, "note": "qwen1.5-72b-chat (Alibaba)" }, { "t": 1716768000000, "y": 1168, "note": "qwen1.5-110b-chat (Alibaba)" }, { "t": 1719619200000, "y": 1207, "note": "glm-4-0520 (Z.ai)" }, { "t": 1722384000000, "y": 1207, "note": "glm-4-0520 (Z.ai)" }, { "t": 1724803200000, "y": 1207, "note": "glm-4-0520 (Z.ai)" }, { "t": 1727395200000, "y": 1257, "note": "qwen2.5-72b-instruct (Alibaba)" }, { "t": 1730073600000, "y": 1275, "note": "glm-4-plus (Z.ai)" }, { "t": 1732233600000, "y": 1274, "note": "glm-4-plus (Z.ai)" }, { "t": 1735516800000, "y": 1315, "note": "deepseek-v3 (DeepSeek)" }, { "t": 1738022400000, "y": 1358, "note": "deepseek-r1 (DeepSeek)" }, { "t": 1740614400000, "y": 1363, "note": "deepseek-r1 (DeepSeek)" }, { "t": 1743292800000, "y": 1370, "note": "deepseek-v3-0324 (DeepSeek)" }, { "t": 1745366400000, "y": 1373, "note": "deepseek-v3-0324 (DeepSeek)" }, { "t": 1747872000000, "y": 1368, "note": "deepseek-v3-0324 (DeepSeek)" }, { "t": 1750723200000, "y": 1424, "note": "deepseek-r1-0528 (DeepSeek)" }, { "t": 1753660800000, "y": 1426, "note": "deepseek-r1-0528 (DeepSeek)" }, { "t": 1756339200000, "y": 1429, "note": "glm-4.5 (Z.ai)" }, { "t": 1759190400000, "y": 1439, "note": "qwen3-max-preview (Alibaba)" }, { "t": 1760659200000, "y": 1440, "note": "qwen3-max-preview (Alibaba)" }, { "t": 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"claude-opus-4-6-thinking (Anthropic)" }, { "t": 1785369600000, "y": 1512, "note": "claude-opus-5-max (Anthropic)" }, { "t": 1787788800000, "y": 1505, "note": "claude-opus-5-max (Anthropic)" }, { "t": 1789257600000, "y": 1508, "note": "claude-fable-5.1-max (Anthropic)" } ] } ], "ann": [ { "t": 1737676800000, "y": 1357, "text": "deepseek-r1 leads", "dy": -14 } ] } }, { "date": "2026-09-12", "url": "https://modevc.com/charts/claude-vs-chatgpt-leaderboard/", "markdown": "https://modevc.com/charts/claude-vs-chatgpt-leaderboard/index.md", "title": "Claude vs ChatGPT: Which Ranks Higher on the Leaderboard?", "dek": "Claude's best model rates 40 points above OpenAI's best on the Arena board. The lead has changed hands 15 times since 2024, usually by a few points.", "claim": "On 11 Sept 2026, Claude's best model rates 40 points above OpenAI's and 16 above Gemini's. Against OpenAI's best it would win about 56% of blind votes.", "sub": "Highest Arena rating among each lab's models on the text board (overall), 9 Jan 2024 – 11 Sept 2026. Arena points.", "note": "\"ChatGPT\" here means OpenAI's models as rated on Arena, not the ChatGPT app. The dataset has no Claude row before 7 Mar 2024. Ratings are relative to the board on each date, so compare lines on the same date, not levels across years. Many lead changes were by a few points, inside the confidence intervals. Win rate uses Arena's 400-point logistic scale.", "take": "The leaderboard flips. The chat history stays put.", "back": "Products that get more useful with every file, memory and workflow a user adds to them.", "keywords": [ "claude vs chatgpt", "claude vs chatgpt vs gemini", "ai model comparison" ], "signal": [ { "source": "Launches", "text": "[Claude Fable 5.1](https://www.anthropic.com/claude-fable-and-mythos-5-1) (1 Sep), Gemini 3.8 Flash (2 Sep) and [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) (3 Sep) shipped in three days." }, { "source": "Arena", "text": "11 Sept 2026: claude-fable-5.1-max is #1. gemini-3.8-flash-high is #6, 16 points back, with overlapping confidence intervals." }, { "source": "Arena", "text": "gpt-6-astra-max entered the board at #61 with 2,059 votes. OpenAI's best model on the board is still gpt-5.5-high (#24)." } ], "read": [ "In the first three days of September, Anthropic shipped Claude Fable 5.1, Google shipped Gemini 3.8 Flash and OpenAI shipped GPT-6 Astra. On the 11 Sept 2026 Arena snapshot, **claude-fable-5.1-max** is #1, and OpenAI's newest model sits at #61. So, Claude vs ChatGPT: which is better, and for how long?", "Today Claude leads OpenAI's best by **40 points**, outside the confidence intervals, which means a win in about 56% of blind votes. Gemini sits between them. Over the longer run, OpenAI led the head-to-head on 71% of days since Claude joined the board on 7 Mar 2024. The lead changed hands **15 times**, 13 of them by fewer than 10 points, and Claude has held it since 6 Feb 2026.", "A user who always chased the top model would have switched assistants 15 times, often for a lead of a few points. Each switch has a cost, and it is what the user has put in: chat history, memory, projects and shared team spaces. A rating lead is a few points and a few months. Accumulated context is a switching cost that grows every week." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://www.anthropic.com/claude-fable-and-mythos-5-1" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "year", "endLabels": true, "series": [ { "name": "Claude (Anthropic)", "short": "Claude", "logo": "si-anthropic", "pts": [ { "t": 1709769600000, "y": 1233, "note": "claude-3-opus-20240229" }, { "t": 1710288000000, "y": 1247, "note": "claude-3-opus-20240229" }, { "t": 1711411200000, "y": 1253, "note": "claude-3-opus-20240229" }, { "t": 1711497600000, "y": 1254, "note": "claude-3-opus-20240229" }, { "t": 1712102400000, "y": 1256, "note": "claude-3-opus-20240229" }, { "t": 1712620800000, "y": 1256, "note": "claude-3-opus-20240229" }, { "t": 1712707200000, "y": 1256, "note": 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OpenAI, which held the most in Apr 2025, holds none.", "claim": "Anthropic holds 8 of the Arena top 20, Google 7 and OpenAI 0. In Apr 2025 OpenAI held 9 and Anthropic 0.", "sub": "Number of models from each lab in the top 20 of the Arena text board (overall), last snapshot of each month, Jan 2024 – 11 Sept 2026.", "note": "Seats count model entries, so variants of one model (thinking, high, max) each take a seat; a lab that lists many variants can hold several seats with one model family. Month-end snapshots only. Snapshots include the models published in the Arena dataset that day; some preview models are missing in early 2025 (no Gemini 2.5 Pro entry appears until mid-2025), so the spring 2025 counts likely overstate OpenAI's share. The other 5 seats on 11 Sept 2026 belong to other labs.", "take": "When two suppliers hold the top tier, your customers are your bargaining power.", "back": "Companies whose customer data and contracts let them negotiate with any model supplier, or switch.", "keywords": [ "anthropic vs openai vs google", "llm leaderboard", "openai vs anthropic" ], "signal": [ { "source": "Launches", "text": "[GPT-6 Astra](https://openai.com/index/gpt-6-astra/) (3 Sept 2026). On the 11 Sept 2026 snapshot its best variant, gpt-6-astra-max, ranks #61 on 2,059 votes." }, { "source": "Launches", "text": "[Claude Fable 5.1](https://www.anthropic.com/claude-fable-and-mythos-5-1) (1 Sept 2026): claude-fable-5.1-max is #1 overall on 11 Sept 2026." }, { "source": "Launches", "text": "Gemini 3.8 Flash (2 Sept 2026) enters the overall board at #6, Google's highest entry." } ], "read": [ "Each of the three big US labs shipped a new model in the first days of September: Claude Fable 5.1, Gemini 3.8 Flash and GPT-6 Astra. A week later, the Arena gives a clear count of who holds the top tier.", "Anthropic holds **8** of the top 20 seats and Google holds **7**. OpenAI holds 0; its last top-20 model dropped out after the 12 Aug 2026 snapshot, and its best model is now #24. In Apr 2025 it was the reverse: OpenAI held 9 seats and Anthropic 0. The three labs together hold 15 seats, up from 5 in Sept 2025, when Chinese labs held 11 of the 20.", "The top tier is now a market with two main suppliers, and the order flipped in 17 months. For a startup, a concentrated supplier market is a pricing risk. The suppliers set the price of the best model, and they can change it. A startup protects its margin with what the suppliers do not have: its own customers and the data from their work." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "OpenAI: GPT-6 Astra announcement", "url": "https://openai.com/index/gpt-6-astra/" }, { "name": "Anthropic: Claude Fable and Mythos 5.1", "url": "https://www.anthropic.com/claude-fable-and-mythos-5-1" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "year", "endLabels": true, "dots": false, "yDomain": [ 0, 20 ], "yTicks": [ 0, 5, 10, 15, 20 ], "matchMs": 1728000000, "series": [ { "name": "Anthropic", "short": "Anthropic", "logo": "si-anthropic", "pts": [ { "t": 1706140800000, "y": 0, "note": "0 of 20 (snapshot 25 Jan 2024)" }, { "t": 1707955200000, "y": 0, "note": "0 of 20 (snapshot 15 Feb 2024)" }, { "t": 1711497600000, "y": 3, "note": "3 of 20 (snapshot 27 Mar 2024)" }, { "t": 1714089600000, "y": 3, 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2026)" }, { "t": 1785369600000, "y": 5, "note": "5 of 20 (snapshot 30 Jul 2026)" }, { "t": 1787788800000, "y": 6, "note": "6 of 20 (snapshot 27 Aug 2026)" }, { "t": 1789084800000, "y": 7, "note": "7 of 20 (snapshot 11 Sept 2026)" } ] } ], "ann": [ { "t": 1745366400000, "y": 9, "text": "OpenAI 9 (Apr 2025)", "dy": -10 } ] } }, { "date": "2026-09-10", "url": "https://modevc.com/charts/how-many-ai-models-are-there/", "markdown": "https://modevc.com/charts/how-many-ai-models-are-there/index.md", "title": "How Many AI Models Are There? Arena Rates 399", "dek": "The Arena text board now rates 399 models from 52 labs. Only 5 labs have a model in its top 20. Supply keeps growing; the top keeps narrowing.", "claim": "Arena rates 399 text models from 52 labs, up from 10 models in May 2023. Yet only 5 labs hold its top 20 seats, down from 15 in Dec 2023.", "sub": "Models with a rating, labs with at least one rated model, and labs with a model in the top 20, Arena text board (overall), 8 May 2023 – 2 Sept 2026. Log scale.", "note": "Counts each listed model, so \"thinking\" or effort variants of one model count separately, and retired models stay on the board. This dataset's 2023 snapshots have no GPT-4 row before 6 Dec 2023 and no Claude row before 7 Mar 2024, so early counts are a floor. Top-20 labs are counted only from the first snapshot with 40+ models. Two models with a blank organisation field are left out of lab counts.", "take": "Models are abundant. The shelf where buyers choose one is not.", "back": "Gateways, evaluation tools and model marketplaces that already sit where teams pick a model.", "keywords": [ "how many ai models are there", "how many llms are there", "ai model comparison" ], "signal": [ { "source": "Launches", "text": "The launch feed logged 11 new text and multimodal models from 8 labs in the 30 days to 10 Sept 2026, including [Claude Fable 5.1](https://www.anthropic.com/claude-fable-and-mythos-5-1) (1 Sep) and [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) (3 Sep)." }, { "source": "Arena", "text": "The 2 Sept 2026 snapshot rates 22 models from 11 labs that were not on the 3 Aug 2026 board." }, { "source": "Arena", "text": "On the same snapshot, 13 labs have a model in the top 50; the other 39 rated labs do not." } ], "read": [ "How many AI models are there? One public answer is the Arena text leaderboard, where people vote blind between two answers. Its 2 Sept 2026 snapshot rates **399 models from 52 labs**, and labs announced 11 new text and multimodal models in the 30 days to 10 Sept 2026. The question for us is whether more supply means more contenders at the top.", "It does not. The board grew from 10 models in May 2023 to 399, and the number of labs from 9 to 52. But the number of labs with a model in the top 20 peaked at **15** in Dec 2023, was 11 in Jan 2025 with 154 models rated, and is now **5**. Anthropic, Google and Meta hold 17 of the 20 seats.", "Model supply is no longer scarce. A buyer's attention is. With hundreds of models and a handful of labs at the top, the hard part for a team is choosing, and the companies that sit at that point of choice collect the traffic. Shipping model number 400 is a weak position; owning the place where teams pick one is a strong one." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Official launch posts (launch feed, captured 7 Oct 2026)", "url": "https://openai.com/index/gpt-6-astra/" } ], "data": { "type": "line", "yFmt": "num", "xFmt": "year", "ylog": true, "yDomain": [ 4, 500 ], "yTicks": [ 5, 10, 20, 50, 100, 200, 400 ], "endLabels": true, "series": [ { "name": "Models rated", "short": "Models", "pts": [ { "t": 1683504000000, "y": 10 }, { "t": 1684713600000, "y": 13 }, { "t": 1687132800000, "y": 16 }, { "t": 1689552000000, "y": 18 }, { "t": 1690934400000, "y": 20 }, { "t": 1693872000000, "y": 23 }, { "t": 1696204800000, "y": 25 }, { "t": 1699401600000, "y": 30 }, { "t": 1700092800000, "y": 31 }, { "t": 1701820800000, "y": 38 }, { "t": 1702598400000, "y": 41 }, { "t": 1703030400000, "y": 42 }, { 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"y": 8 }, { "t": 1784592000000, "y": 8 }, { "t": 1785024000000, "y": 7 }, { "t": 1785110400000, "y": 7 }, { "t": 1785369600000, "y": 7 }, { "t": 1785628800000, "y": 6 }, { "t": 1785715200000, "y": 6 }, { "t": 1785974400000, "y": 6 }, { "t": 1786320000000, "y": 6 }, { "t": 1786406400000, "y": 6 }, { "t": 1786492800000, "y": 6 }, { "t": 1787097600000, "y": 6 }, { "t": 1787270400000, "y": 6 }, { "t": 1787788800000, "y": 6 }, { "t": 1788220800000, "y": 6 }, { "t": 1788307200000, "y": 5 } ] } ], "ann": [ { "t": 1703030400000, "y": 15, "text": "15 labs in top 20", "dy": -12 } ] } }, { "date": "2026-09-09", "url": "https://modevc.com/charts/best-llm-for-coding/", "markdown": "https://modevc.com/charts/best-llm-for-coding/index.md", "title": "Best LLM for Coding: The Arena Coding Leaderboard", "dek": "Anthropic has had the top coding model on every Arena snapshot for 9 months. Two lighter models from other labs are now inside its margin of error.", "claim": "claude-opus-4-6-high leads Arena coding at 1535. Anthropic has held #1 for 280 days, but Z.ai and Google sit within the interval, 19 points back.", "sub": "Best coding model from each lab in the Arena coding top 50, 2 Sept 2026. Bar = Arena points above the #50 coding model (kimi-k2.5-thinking, 1474).", "note": "Arena coding is a category of the Arena text board: blind human votes on prompts that involve code. It measures which answer people prefer, not whether the code runs or passes tests. \"Interval overlaps #1\" means the model's 95% upper bound reaches the leader's lower bound; those ranks are not settled. Light-tier models with few votes have wide intervals. Latest snapshot on or before 9 Sept 2026. One row per lab (its best model); other variants of the same lab are omitted.", "take": "In coding, the lead has lasted 9 months. The challenger is already inside the margin of error.", "back": "Coding products that own the review, test and deploy loop, where the model is one part of the job.", "keywords": [ "best llm for coding", "best ai for coding", "coding llm leaderboard" ], "signal": [ { "source": "Launches", "text": "[GLM-5.3-Flash](https://docs.z.ai/guides/vlm/glm-5.3-flash) (26 Aug 2026): Z.ai's light model is the best non-Anthropic coder on this snapshot, #7." }, { "source": "Launches", "text": "Gemini 3.8 Flash (2 Sept 2026) enters the coding board at #8 with 1,456 votes, so its interval is still wide." }, { "source": "Arena", "text": "On the 2 Sept 2026 coding board, 6 Anthropic models rate above the best model from any other lab. OpenAI's best, gpt-5.4-high, is #21." } ], "read": [ "Two light models launched in late August and early September: Z.ai's GLM-5.3-Flash and Google's Gemini 3.8 Flash. Both went straight into the Arena coding top 8. So which LLM is best for coding right now, and how sure can we be?", "Anthropic holds the top spot with **claude-opus-4-6-high** and has led the coding board on every snapshot for **280 days**. It holds 7 of the top 10. But the best Z.ai model is only 19 points behind, so it still wins about **47%** of head-to-head votes.", "A 9-month lead is long by Arena standards, and it was earned with developers. Developers are also the users who try a new model fastest. When a cheap challenger is inside the margin of error, the lead will not hold through rankings. It holds through the tools where developers already work every day." ], "sources": [ { "name": "Arena leaderboard dataset (lmarena-ai, CC BY 4.0)", "url": "https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset" }, { "name": "Z.ai GLM-5.3-Flash release note", "url": "https://docs.z.ai/guides/vlm/glm-5.3-flash" } ], "data": { "type": "hbar", "fmt": "pts", "labelHead": "Best model (lab)", "valueHead": "Points above #50", "rows": [ { "label": "claude-opus-4-6-high (Anthropic)", "v": 61.5, "logo": "si-anthropic" }, { "label": "glm-5.3-flash (Z.ai)", "v": 42.2, "logo": "lh-zai" }, { "label": "gemini-3.8-flash-high (Google)", "v": 42.1, "logo": "si-google" }, { "label": "kimi-k3-max (Moonshot)", "v": 36.7, "logo": "lh-moonshot" }, { "label": "mimo-v2.5-pro (Xiaomi)", "v": 26.6, "logo": "si-xiaomi" }, { "label": "qwen3.8-max (Alibaba)", "v": 24.5, "logo": "si-qwen" }, { "label": "muse-spark-1.2 (xHigh) (Meta)", "v": 22.3, "logo": "si-meta" }, { "label": "gpt-5.4-high (OpenAI)", "v": 21.5, "logo": "lh-openai" }, { "label": "ernie-5.1 (Baidu)", "v": 14.9, "logo": "lh-baidu" }, { "label": "amazon-nova-experimental-chat-26-02-10 (Amazon)", "v": 11.2, "logo": "lh-aws" }, { "label": "grok-4.5 (xAI)", "v": 8.7, "logo": "lh-xai" } ] } } ] }