Introducing System One Models & Jev

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   A System One model writes no text: it takes a state and questions with predefined answers and returns, for each, an option plus a calibrated probability — a decision code can use directly, in a single pass.

   Introducing System One Models & Jev · 2026

   A System One model returns an option from your list and how sure it is; it cannot invent options, but it can pick wrong.

   The name comes from Kahneman's psychology: "System 1" is fast judgement, "System 2" is slow reasoning. TypeSafe AI launched Jev (named after Jevons' paradox) on 15 September 2026 on exactly that split: an LLM reasons and generates, Jev only decides. The contract has three primitives. "Choice" picks one option from a list you supply and returns a probability for each; "Score" places the state on an ordinal scale described in words; "Noul" is a yes/no with P(true). All are evaluated in parallel, in one pass, not token by token. The training, which the company calls RLCD — Reinforcement Learning for Calibrated Decisions — optimises the probabilities to match reality: if the model says 0.9 on a hundred inputs, about ninety should be true. That is where the usefulness in software comes from: a threshold on a probability becomes an "if" that means something. The numbers, with their labels. The company claims "193.6x faster, 444.6x cheaper" than GPT-6 Astra and Fable 5.1 on four internal evaluations; stated latency is 70–500 ms, the price $0.042 per million input tokens. Independently, Every measured in September 2026 a median of 0.35 s per passage against 8.83 s for Fable, and on planted defects Jev caught six of seven, the large model all seven. "Zero hallucination" only means the schema is guaranteed: the model cannot invent an option, but it can pick the wrong one from your list.

   “Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.”

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