Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
TypeSafe Jev: State and Typed Questions In, Choice, Score and Probability Out
TinyRouter highlights TypeSafe's Jev model: state and typed questions go in, choice, score or probability come out, with no prose to parse. A sample call returns choice=billing (p=0.71) and score 0.8 via POST /v1/systemone, with 32K context and free output.
Text was never the hard part. Parsing it was. Jev by TypeSafe: state + typed questions in, choice/score/probability out — no prose to parse. Real call → choice=billing (p=0.71), score 0.8. POST /v1/systemone, 32K ctx, output free. #Jev #TypeSafe #DecisionModels
· 0 likesOpen on X
More like this
Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
