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 Returns Probabilities over Fixed Answers
The post explains that Jev takes the current state and questions with predefined answers, returning probabilities over those answers; labels are defined by the app, so the model cannot invent new ones.
Jev takes a state, the current situation, plus questions whose answers you fix in advance. It returns probabilities over those answers. Your app defines the labels, so the model cannot invent new ones.
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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.
