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API & ModelsJev 1.13
jev-1.13.0
The current versioned Jev build: $42 per billion input tokens, a 64k context, and 250,000 tokens per second.
- Category
- API & Models
- Also known as
- Jev 1.13
- Related terms
- 5
- Directory entries
- 15
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
jev-1.13.0 is the model behind both aliases. Its model card lists $42 per Btok input with free output, rate limits of 250,000 tokens per second and 1,200 requests per minute, and a 64k context split as 32k for state plus the longest question.
It is also the version the jaggedness page tracks: literal reading, unreliable counting, dates as text, indirection, context rot, and structural invariants that do not hold. Many of those are expected to be fixed in later versions.
Tagsapimodels
Related terms
Definitions that connect to this one.
jev-latestThe default model alias in the SDKs and docs: the most recent stable official Jev release, currently jev-1.13.0.API & ModelsModel aliasA name like jev-latest that resolves to a versioned model ID and can move when a new release ships.API & ModelsToken pricingJev's published rate: $42 per billion input tokens, with output tokens free.API & ModelsContext windowThe 64k-token budget covering state plus all questions, with a 32k sub-budget for state plus the longest question.API & ModelsJaggednessThe documented set of jev-1.13 failure modes — literal reading, counting, dates, indirection, context rot — each with a guardrail.Evaluation & Training
From the directory
The model card for jev-1.13.0: $42 per billion input tokens with free output, a 64k context, 250k tokens per second, and how to list the models your account can call.
Sites & GuidesDocs#official#docs#models
Official
A maintained list of jev-1.13's known failure modes, literal reading, unreliable counting, dates as text, indirection, context rot, and contradictory criteria, each with a guardrail.
Practices & PatternsDocs#official#docs#evaluation
Official
TypeSafe AI's product site for Jev, its first System One Model: typed decisions with calibrated confidence, performance and pricing claims, a FAQ, and links to the docs, console, workflow evals, and launch post.
Sites & GuidesDocs#official#docs#models
Official
A TypeSafe blog essay arguing that in ML the order that matters is doing the right task, then data, then compute, then algorithms, using the InstructGPT result as its example.
Sites & GuidesArticle#official#article#models
Official
Cloudflare Workers AI's model page for typesafe/jev: run the structured evaluation model through an env.AI.run binding or the REST /ai/run endpoint, with typed Noul, Choice, and Score questions and worked examples.
Tools & IntegrationsDocs#community#cloudflare#integration
Community
Wikipedia's article on Jev: TypeSafe AI, the September 15, 2026 early-access release of jev-1.13.0, the Choice, Score, and Noul primitives, RLCD training, and the unpublished weights and technical paper.
Sites & GuidesArticle#community#article#models
Community
9 more matching entries in the full directory.
From the community
Posts from builders shipping with Jev right now.
GPT and Jev compared live
LitJev: Jev-style decisions on any local LLM
开源了 LitJev：我们对 Jev 类型化决策 API 实现方式的猜想，可接入任意现成 LLM。 小测试：Qwen3.8-27B，10 道 MMLU-Pro 一次完成，0.3 s。比让模型只生成答案快 2.9x；比生成完整 schema JSON 快更多（显然）。更多测试马上来。 非官方，基于公开信息。
Secret detection with Jev, tested
Built a quick Secret Detection Test with Jev by @typesafeai Results seem promising so far, ran it a few times and it's pretty consistent as well. github.com/teyhouse/jev-s…
Jev Driving Lab: a playground for System One models
@typesafeai (and System One models in general) have a lot of uses, and fun to play with. New paradigm open to public now. This is a self-driving sim I spun up to test Jev today. Structured sensor state in, typed driving decisions out, steer, brake, overtake, pedestrians, speed Show more
200 rows, 32 template families, and traps, judged in one call
First test with Jev (TypeSafe System One): 200 rows, 32 template families, traps — 'yes' rows with "we'll pass", 'no' rows with 'absolutely, happy to meet'. The single Jev question scored 100%. My 12 dimensions scored 98%. I had designed a task my own question could ace.
jev(): Postgres WHERE clauses in plain language
I think I just cooked something 🔥 jev(): a PostgreSQL extension that searches your whole database in natural language. No index, no embeddings, just one function. WHERE jev(people, 'could work from home') or WHERE jev(people, 'name sounds european') 129 rows judged in ~1s Show more
@CompleteSkeptic
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