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.
Jev Model: Typed Questions Instead of Text Generation
The Jev model takes state and a handful of typed questions (choice, score, yes/no) and returns probabilities with confidence in 70-500 ms, with no prose and no parsing.
Most of the AI discourse still assumes every model has to write something back. #Jev just dropped that assumption. You hand it state + a handful of typed questions (choice, score, yes/no) and it returns probabilities with confidence in 70-500 ms. No prose. No parsing. Output
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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.
