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Covered in Agents & ProtocolsModels & BenchmarksAI Engineering, where the background and the sources for this subject live.
Two shipped integrations put Jev inside an agent loop, from Pydantic AI and LangChain. Both leave three things to your code: a ledger of actions already executed, because Jev may re-propose a tool that ran; a per-question projection of state, because accuracy falls as irrelevant context grows; and the confidence threshold that escalates to a language model.
5 min read
Jev posts state and a map of questions to one endpoint and returns typed answers carrying probabilities. TypeSafe claims 0% type errors, which is a guarantee about schema conformance rather than about correctness. A noul of 0.95 is a well-formed number that can still be wrong about the thing it scored.
3 min read
Jev answers noul, choice and score questions. Choice caps at 255 options and score at 10 levels, and both return a confidence derived from their probability distribution. Noul returns no confidence field at all, because the probability it hands back already describes the uncertainty. Modelling the problem means picking among those three.
4 min read
TypeSafe AI launched Jev on 15 September 2026 as a System One model, priced at $0.042 per million input tokens with output free, and reports 70ms to 500ms against 3 to 329 seconds for frontier models. The economics are the strong part of the claim. The psychology metaphor carries less weight.
4 min read
A large context window raises the ceiling on how much a model can see at once, but accuracy is not uniform across that window. Retrieval quality, latency and cost still dominate real workloads. Long context is best treated as a way to stop chunking small documents rather than as a replacement for retrieval.
5 min read