Jev model answers structured questions in parallel in 0.114 seconds
A new model answers preset options in parallel with a confidence score, claimed to be 294x cheaper and 195x faster than Sonnet 5, with an API waitlist and a web demo.
man who co-invented ChatGPT just shipped a model that does in 0.114 sec what GPT Astra does in 8.5. i wired Jev into the routing layer of a build i was running on Astra: same task finished 37 MIN SOONER, zero malformed responses to retry. you hand it a situation and a list of typed questions. it answers all of them at once, in parallel, with a confidence number attached to each. no chain of thought, no tokens streaming out one by one. you stop parsing a JSON blob back into your code and praying it validates, because the model physically cannot answer outside the options you defined. the numbers TypeSafe publishes for it: > $0.042 per million input tokens, and output tokens are free > same accuracy as Sonnet 5 on their eval, 67.8% for both, at 294x less cost and 195x the speed > 0.114 seconds against 8.566 for GPT-5.6 Terra on the same demo > their Doom demo has it making around 10 decisions per second $40M seed led by DCVC, and the name is a nod to Jevons, the economist who showed that cheaper coal meant burning more of it, not less. this runs underneath the agent, on the calls that happen ten times a second and never get read by a person. waitlist for the API, but the browser playground is open now. links below.
This post quotes another post: “ChatGPT co-inventor releases Jev, a model built for decisions rather than text”