--- title: Open models hit 62% of token usage, and nobody saw it coming description: Open-weight AI models jumped from 28.4% to 62% of token traffic on Vercel's AI Gateway between June and August 22, according to Vercel CEO Guillermo Rauch. He... canonical: https://daily.dev/posts/open-models-hit-62-of-token-usage-and-nobody-saw-it-coming-kr9tmxeor twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Open models hit 62% of token usage, and nobody saw it coming | daily.dev og:description: Open-weight AI models jumped from 28.4% to 62% of token traffic on Vercel's AI Gateway between June and August 22, according to Vercel CEO Guillermo Rauch. He... og:url: https://daily.dev/posts/open-models-hit-62-of-token-usage-and-nobody-saw-it-coming-kr9tmxeor og:image: https://api.daily.dev/og/posts/Kr9TmxEor.png og:image:alt: Open models hit 62% of token usage, and nobody saw it coming og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Open models hit 62% of token usage, and nobody saw it coming **[Trends](https://daily.dev/sources/trends)** · 2 min read · 5 upvotes · 0 comments ## Summary Open-weight AI models jumped from 28.4% to 62% of token traffic on Vercel's AI Gateway between June and August 22, according to Vercel CEO Guillermo Rauch. He argues this is early-stage momentum since most developer tooling (CLIs, IDEs, SDKs) is still hardcoded to closed models, and once tooling becomes model-agnostic, adoption could accelerate further. Enterprises are driving the shift for cost reasons: AT&T reportedly cut inference costs by 56% and Coinbase by around 50% by routing to open models. A SemiAnalysis chart shows the quality gap between open and closed frontier models has narrowed to about 3.3% on Stanford's benchmarks, or roughly four months per Epoch AI estimates. ## Content The number that's driving the conversation: open-weight models went from 28.4% to 62% of tokens on Vercel's AI Gateway between June and August. Two months. That's not a trend line, that's a cliff. Vercel CEO Guillermo Rauch called August 22 a record day and noted this is "very likely just the start" because enterprise adoption is still early and most tooling isn't model-agnostic yet. That last part matters — the 62% figure is happening *before* the infrastructure fully catches up. Chamath put the pricing reality bluntly: "A barrel of intelligence" (his term for one million tokens) costs $26 from OpenAI, $56 from Anthropic, $1 from xAI, $1.50 from Meta, and $0.50 from Chinese providers. "If you made an early bet on one of the companies selling extremely expensive barrels of intelligence, and you try to pass those costs through to customers, you may run into downstream difficulties." Enterprises are already acting on this. AT&T cut costs 56% by routing to cheaper models. Coinbase saved roughly 50%. Cisco built an agent system for 90,000 employees where 50-60% of requests go to open-weight models, with only a small slice hitting frontier closed models — and that's by design, not accident. The multi-agent pattern is accelerating this. As one ML researcher pointed out, in supervisor-plus-subagent architectures, the subagents handle most of the token volume. Frontier open models are "beautifully" suited for that role — cheaper, fast enough, and good enough. There's no reason to run Claude Opus on every subagent call when a smaller model handles it fine. The quality gap is also closing faster than expected. Six months ago, Claude Opus 4.6 was state of the art. Today, Qwen 3.8 — a 27B model that runs on a gaming GPU — beats it on some coding benchmarks. The gap Stanford measured at 3.3%, Epoch AI at roughly four months of lag. One real caveat: "open" is getting complicated. Kimi K3 and MiniMax M3 require revenue-based licensing agreements. DeepSeek V4 and GLM 5.2 use permissive MIT licenses. Downloadable weights don't automatically mean unrestricted commercial use. Enterprises building on this shift should read the licenses before assuming they're in the clear. But the direction is set. The token market has already flipped. ## Questions this post answers ### What percentage of token traffic on Vercel's AI Gateway comes from open models now versus a few months ago? Open models went from 28.4% of token traffic on Vercel's AI Gateway in June to 62% by August 22, according to figures shared by Vercel CEO Guillermo Rauch. He characterizes this as early-stage growth rather than a ceiling, since much of the developer tooling ecosystem (CLIs, IDEs, SDKs) is still hardcoded to specific closed models, a constraint that is expected to loosen over time. _Track shifts like the open-model surge as you decide which models to build workflows around, on daily.dev._ ### How much money have companies saved by switching from closed to open AI models? AT&T reportedly cut inference costs by 56% by routing to cheaper open models, and Coinbase saved around 50% doing the same. These enterprise cost reductions are cited as the primary driver behind the rapid shift in token usage toward open models, rather than any philosophical preference for openness. _Compare real-world cost outcomes like these before committing budget to a model provider, via daily.dev._ ### How close are open-weight AI models to closed frontier models in quality? Open models now trail closed frontier models by about 3.3% according to Stanford benchmark data cited in a SemiAnalysis chart, or by roughly four months according to Epoch AI's estimate. This is a sharp narrowing from a few years ago when the consensus was that open models could never catch up to closed labs. _Keep tabs on benchmark gaps like this when picking a model for production, using daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion and 1 comments across x (as of 2026-08-28). **TL;DR:** The single available reply frames the shift to open-weight models as a straightforward cost-driven business decision, not a surprise. **Sentiment:** 40% positive · 20% mixed · 40% skeptical **The case for** - Adopting cheaper open-weight models is a rational cost-saving strategy for businesses. **By community** - x (mixed): A lone reply argues low cost is the deliberate strategy behind adoption, not an unexpected trend. **Highlights** > @trikcode Low cost is the whole point though US businesses adopting because its cheap isnt a surprise, its the strategy > — [HarryTandy on x](https://x.com/HarryTandy/status/2093373747306672265) **Source threads** - [x](https://x.com/trikcode/status/2093372305887862895) · 0 points · 1 comments --- Tags: [#vercel](https://daily.dev/tags/vercel), [#finops](https://daily.dev/tags/finops), [#ai-inference](https://daily.dev/tags/ai-inference), [#ai-gateway](https://daily.dev/tags/ai-gateway) [View this post on daily.dev](https://daily.dev/posts/open-models-hit-62-of-token-usage-and-nobody-saw-it-coming-kr9tmxeor) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Open models hit 62% of token usage, and nobody saw it coming","url":"https://daily.dev/posts/open-models-hit-62-of-token-usage-and-nobody-saw-it-coming-kr9tmxeor","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/open-models-hit-62-of-token-usage-and-nobody-saw-it-coming-kr9tmxeor"},"datePublished":"2026-08-23T00:05:35.991Z","dateModified":"2026-08-28T19:34:20.023Z","description":"Open-weight AI models jumped from 28.4% to 62% of token traffic on Vercel's AI Gateway between June and August 22, according to Vercel CEO Guillermo Rauch. 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He characterizes this as early-stage growth rather than a ceiling, since much of the developer tooling ecosystem (CLIs, IDEs, SDKs) is still hardcoded to specific closed models, a constraint that is expected to loosen over time. Track shifts like the open-model surge as you decide which models to build workflows around, on daily.dev."}},{"@type":"Question","name":"How much money have companies saved by switching from closed to open AI models?","acceptedAnswer":{"@type":"Answer","text":"AT&T reportedly cut inference costs by 56% by routing to cheaper open models, and Coinbase saved around 50% doing the same. These enterprise cost reductions are cited as the primary driver behind the rapid shift in token usage toward open models, rather than any philosophical preference for openness. Compare real-world cost outcomes like these before committing budget to a model provider, via daily.dev."}},{"@type":"Question","name":"How close are open-weight AI models to closed frontier models in quality?","acceptedAnswer":{"@type":"Answer","text":"Open models now trail closed frontier models by about 3.3% according to Stanford benchmark data cited in a SemiAnalysis chart, or by roughly four months according to Epoch AI's estimate. This is a sharp narrowing from a few years ago when the consensus was that open models could never catch up to closed labs. Keep tabs on benchmark gaps like this when picking a model for production, using daily.dev."}}]} ```