Jev Agent

Jev is a System One model built by TypeSafe AI. Instead of generating text token by token, Jev evaluates a state — text, a JSON object or an array of strings — against a map of typed questions and returns one structured answer per question, with probabilities attached, in a single parallel pass.

TypeSafe prices Jev at $0.042 per million input tokens and does not charge for output, because Jev returns typed decisions rather than generated text. The Rundown AI puts Jev at 40–400x cheaper than frontier LLMs, and TypeSafe's own launch post reports up to 444.6x; both are upper ends, not figures every workload will see.

Yes. Jev fits the fast inner loop of an agent: routing a request, picking the next tool, checking whether a step succeeded or deciding when to escalate to a larger model. Jev Agent access for these workflows comes with any paid plan.

No. Jev Agent is an independent platform and is not affiliated with or endorsed by TypeSafe AI. Jev is a trademark of TypeSafe AI, and the official documentation lives at docs.typesafe.ai.

Jev Agent bills only the input tokens Jev reads; output is free because Jev returns typed decisions instead of generated text. LLMs bill both input and output, and TypeSafe notes that LLM output tokens typically cost about five times as much as input. The calculator on this page compares both for your volume.

Requests are JSON with a state — a string, a JSON object or an array of strings — and a map of named questions. Each answer comes back under the same name as a typed object: a choice with per-option probabilities, a score on the levels you define or a Noul probability, with a confidence value on choice and score answers. Jev currently accepts text input only.

No. An autoregressive LLM produces output one token at a time. Jev is a non-generative System One model: it evaluates the whole state against your typed questions in one parallel pass and returns structured answers with probabilities, not free text.