Why I couldn't build Jev at OpenAI — Diogo Almeida
Diogo Almeida · TypeSafe
Decide API live — jv_live_ keys, try free
Jev AI returns probabilities and typed labels your code can branch on — Choice, Score, Noul — not chat prose. Hosted decide API for classify, route, score, and moderation.
Independent third-party host (not TypeSafe official). Instant jv_live_ keys — start free, then prepaid packs from $5.
Free: 5 runs without signup · 10,000 tokens/month on signup · 3 People Search lookups
Live example
A real support message — several typed decisions in one call.
sample · edit & run for live API
Community explainers
From browser agents to routers and serving — the decision layer plugs in without replacing your generative models.

Run a live decide below — including a zero-token Image / Box beta preview and game previews — then open the full Jev Playground for production templates (5 free anonymous runs). After a run you like, copy the same pattern into the API.
Jev Workbench
Pick a scenario, tweak the input, run the real API — then copy the pattern into your app.
Local preview · jev-snake (no API / $0)
Live preview · local heuristic
$0 · no tokensmoves
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last
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Auto-playing with a local heuristic so the homepage stays free. The full game page uses real decide calls (rate-limited demo).
Decision stream
preview · not billed
Jev Snake
* Simulated latency for the preview. Full page = real API round-trip.
Open full Snake demo →Jev AI is TypeSafe's System One decision model: you send state plus typed questions, and get Choice, Score, or Noul answers with calibrated confidence — probabilities and labels your code can branch on, not chat prose. This website is an independent third-party host (not the official TypeSafe product): try the playground free, then ship with prepaid jv_live_ keys against /api/v1/decide.
TypeSafe introduces Jev as a System One model aimed at structured decisions — labels and probabilities software can act on — rather than open-ended chat generation.
Deeper explainer: System One model · FAQ · API docs
Jev’s three primitives — typed answers your agent code can branch on, not chat strings.
Choice
Pick one label from a closed set — route, allow/block, escalate.
Score
Ordered scale for urgency, risk, or quality your code can threshold.
Noul
Calibrated probability for yes/no gates without free-form prose.
Bounded decisions your code can act on — not another chat window.
Support Triage
Automatically classify, prioritize and route support tickets.
Agent Safety
Detect risky actions before an AI agent acts.
Content Moderation
Classify content into allow / review / block.
Lead Scoring
Score inbound leads and route high-value prospects.
RAG Relevance
Decide whether retrieved documents are relevant.
LLM Routing
Choose the cheapest model capable of handling a request.
Free anonymous trial (5 runs) → prepaid packs from $5 (≈ 11.9M input tokens) → production balance. Input metered; output free on this host. Retail band about $0.175–$0.42/M.
This is an unofficial community resource. We are not affiliated with TypeSafe AI.
Jev is the System One decision model introduced by TypeSafe AI. It is designed for AI agent pipelines where predictable, consistent decisions matter more than creative text generation. The core design philosophy is simple: Decisions not strings.
This unofficial host issues instant jv_live_ keys and meters prepaid capacity for /api/v1/decide, /api/v1/batch/decide, a free /api/v1/gate, and ready-made product paths — so you can ship agent routing without waiting on the official TypeSafe waitlist. Upstream keys stay on our servers.
Source reference: TypeSafe AI official announcement blog post.
This is an unofficial community resource. We are not affiliated with TypeSafe AI. Instant jv_live_ keys, prepaid metering, playground and batch tooling. Upstream model access stays on our servers. See Terms, Privacy, and FAQ.
Short explainers for What is Jev AI?, how to use it, and what you can build. Play on this page (needs YouTube access).
Diogo Almeida · TypeSafe
Greg Isenberg
Hands-on review
The Code Report
Charts based on published TypeSafe ranges. Full report with methodology and scenario templates lives on the benchmarks page.


Six visual cards covering the talking points that show up again and again in public Jev intros: decisions instead of chat text, smart if-style routing, Choice / Score / Noul, parallel questions, calibrated confidence, and schema-safe outputs.






The System One / System Two naming comes from behavioral psychology. In agent engineering terms, the split maps cleanly to two different jobs inside one pipeline. See the glossary for short definitions.
Production stacks rarely pick only one. Most teams put Jev at the decision edge, then call a generative model only when the chosen path needs prose, tool planning, or deeper reasoning. Read the comparison page for a feature-by-feature breakdown.
Demo → copy API → signup → prepaid key. 14 use-case guides and a live playground are ready now.
People SearchTutorialExamplesTypeScriptPythonCost calculatorJev vs GPT
Concrete figures from this site's billing and latency bands (see benchmarks). Input is prepaid retail on this host; output tokens are free.
Latency band
70–500 ms
Published System One range (not chat-second latency)
Input pricing
≤ $0.42/M
This host retail packs; output free
Free trial
5 runs
Anonymous real-API tool runs, no signup
Monthly grant
10,000
Input tokens for signed-in accounts
Starter pack
$5
≈ 11.9M input tokens
Workflow evals
~194× / ~445×
Published speed / cost framing vs chat LLMs — methodology