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.
Scoring relevance with Jev and balancing token budget
The post describes using Jev to score how relevant each passage is to a question, then balancing relevance, novelty and cost to fill the token budget.
Score how relevant each one is to your question using Jev. Finally, fill the token budget by balancing how relevant, new and expensive each passage is.
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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.
