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 speeds up decisions in Orior: from ~8s to under 1s
The author shares hands-on use of Jev in Orior: decision steps dropped from ~8 seconds with Gemini 3.8 Flash to under 1 second. Jev doesn't write text; it takes a situation and possible answers, returning a choice, score, or yes/no judgment.
Jev probably isn’t useful to you. we used it in Orior and took decision steps from ~8 seconds with Gemini 3.8 Flash to under 1 second. both can be true. Jev doesn’t write text. you give it a situation and the possible answers. it returns a choice, a score or a yes/no judgment,
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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.
