TypeSafe AI · Independent

Jev (TypeSafe)

A decision model, not a chatbot: the fast "smart if" layer of an agent stack. Jev is TypeSafe's "System One" model, built for fast, structured decisions inside software and agents. It doesn't chat or write code. You send it a state (text or JSON) plus typed questions, and it returns typed answers with probabilities and confidence that your code can act on directly.

✓ Last verified October 15, 2026 from official sources

Pricing

$0.042 per million input tokens ($42 per billion). Output tokens are free. Jev 1.14 and 1.13 are priced the same. Rate limits are set per organization; higher limits on custom and enterprise plans.

Best for

Routing requests to the right handler, whether code, a specialist LLM or a human. Guardrails that screen messages going into and out of an LLM app. Classification, scoring and confidence-gated "smart if" statements in agent workflows.

Privacy · Skill

Cloud API. TypeSafe says Jev isn't trained on customer data; enterprise customers can get zero data retention. Skill level: Advanced.

What it's great at

Cheap, calibrated decisions on every agent turn
Confidence-gated routing: act, escalate to a bigger LLM, or ask a human
Guardrails, re-ranking and classification with typed outputs

What it can do

Three question types: Choice (pick one option, with per-option probabilities and confidence), Score (rate against a rubric you define) and Noul (the probability that a yes/no statement is true)
Many questions per request, all evaluated in parallel against one state ("speculative fan-out")
Jev 1.14.0 (released Oct 14, 2026, as jev-preview) brings small improvements across the board, mostly in multilingual support. TypeSafe says jev-latest will move to 1.14 a week after release.
Experimental "danger zone" features behind API headers: image inputs, a new Box type (a bounding box) and long context up to 262k tokens
Context of 64k tokens per request and 32k per question by default. Same weights for every account; TypeSafe says it doesn't train on customer requests.

Example tasks to try

A Choice question routes each support ticket to billing, technical or sales, and anything below your confidence threshold goes to a human.
A Noul question checks whether a quote's source actually supports the claim before your agent cites it.
Score each retrieved passage, and decide in code which ones reach your answering model.

Start in 5 minutes

Get an API key from the TypeSafe console (console.typesafe.ai)
Try it in the browser Playground: paste some text as the state and add a Choice or Noul question
Or install the TypeSafe agent skill so Claude Code or another coding agent can write the integration (npx skills add typesafe-ai/skills --skill typesafe-ai)
Pin the model ID (jev-1.14.0 or jev-1.13.0) if you tune confidence thresholds, because the jev-latest and jev-preview aliases move

Go to TypeSafe AI's official page →

Watch out for

It can't replace the LLM behind Claude Code, Cursor or Grok Bot. Use it alongside one. English is its strongest language. TypeSafe flags the danger-zone features (images, Box, long context) as not production-ready.

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Official sources

Prices change often. Confirm on the vendor's page before you buy. We're independent, and no vendor pays for placement.