Introducing System One Models & Jev
Introducing System One Models & Jev is TypeSafe AI's launch post, written by founder Diogo Almeida, who worked on the instruction-following research behind ChatGPT. Jev gives up generating text. You send it a state (a document, a record, a few named fields) and a batch of questions, and every answer comes back at once as a typed value with a calibrated probability: yes or no, one of a set of options, or a score. TypeSafe charges $0.042 per million input tokens, makes output free and claims answers in 70 to 500 milliseconds; its manifesto argues that today's models are clever enough already and the bottleneck is that intelligence is hard to build on. Simon Willison's write-up prefers the name decision models, likes them for classification and search reranking, and worries that a model returning only a number is an even blacker box than an LLM. A benchmark video has a 22-million-parameter local classifier beating Jev on Banking77, 93% to 80%, while granting that nothing older takes new instructions with every query. A fast, cheap, typed if-statement is useful, and worth trusting as far as your evals go.
Was this useful?