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's Complete Architecture: Zero-Token Parallel Evaluation via State and Typed Questions
The post outlines Jev's complete architecture: input is your current state (goal, progress, evidence) plus typed questions attached to it, with no prompt or chat history; processing generates zero tokens as Jev evaluates every question in parallel.
Jev's complete architecture, in one post: Input: your current state (goal, progress, evidence) plus a set of typed questions attached to that state. No prompt, no chat history. Processing: zero token generation. Jev evaluates every question against the state in one parallel
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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.
