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: Don't Generate, Decide
Jev takes context and a specific question, returning a structured decision with a probability instead of generating text, useful for model routing and agent workflows.
Most AI models are built to generate. But Jev is trying something different: Don’t generate. Decide. Give it some context and a specific question, and it returns a structured decision with a probability. I found this interesting for things like model routing, agent workflows,
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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.
