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CommunitySites & GuidesArticleVerified 2026-09-22

Jev: TypeSafe's System One Model Explained

DataCamp's explainer walks the eval tables behind System One models, from 67.8% workflow accuracy and 0% structured-output errors to the $0.042 per million token price and how to call the API.

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2026-09-22
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Highlights

  • On TypeSafe's four-workflow benchmark Jev scores 67.8% at $0.0004 per case and 0.4s, against GPT-5.6 Terra's 67.9% at $0.0304 and 10.1s.
  • Vendor tables put structured output errors at 0% for Jev against 0.58% for luna and terra, 5.73% for Opus 5, and 45.5% for Claude Haiku 4.5.
  • Tool call errors reach 17.0% for GPT-5.6 Sol; Jev reports 0% by construction.
  • The API is POST https://api.typesafe.ai/v1/systemone with model jev-latest and Python and JavaScript SDKs; access is waitlisted early access.
  • DataCamp notes the eval workflows and reference answers were written by TypeSafe, so accuracy parity is promising rather than settled.

Watch out

Vendor-reported numbers with no independent reproduction at publication, and the article itself flags its reliance on TypeSafe's tables.

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From the community

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Follow @typesafeai

Vercel's fx safety reviewer, 18x faster

We're seeing extraordinary results from @typesafeai. Default mode in 𝚏𝚡 is auto, with a safety reviewer analyzing every command. That reviewer runs on GPT Luna today. Jev is up to 18x faster (p95) *and* more accurate. It's coming to @vercel AI Gateway and likely new default.

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We benchmarked fx auto mode (safety) classifier with @typesafeai's Jev. tl;dr: ~5-18x faster and more accurate than 𝚐𝚙𝚝-𝟻.𝟼-𝚕𝚞𝚗𝚊, our current top choice

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After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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