Jev returns structured decisions in 80ms instead of generating text
On the same support ticket, Jev returns team, urgency, and refund decisions in 80ms, while a normal model takes 914ms to type the same JSON token by token.
Diogo Almeida left OpenAI and built Jev, a model that refuses to write a single word. It answers in 80 ms. Same support ticket, same three questions. Jev returns team, urgency, and refund in 80 ms. A normal model types that exact JSON in 914 ms, token by token. Not a wrapper. Not a distill. Not a small LLM with a schema stapled on. It reads the state once and pulls all three answers off the model layers in parallel. Nothing to parse, nothing to retry. The part nobody talks about: it cannot write a sentence. 32K context, no images, 255 options per choice, every number is TypeSafe's own, and access is a waitlist. 140,000 people joined it in 36 hours. Input is $0.042 per million. Output is zero because there is no output to generate: client.decide(model="jev-1", state=ticket, questions={"intent": ["refund","bug","billing"], "urgency": ("score",1,5)})
This post quotes another post: “ChatGPT co-inventor releases Jev, a model built for decisions rather than text”