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 slot: inner-loop judgment, not a second brain
Jev's slot is not a second brain but inner-loop judgment: state in, typed answers out, code owns the rest. LLM still writes, Jev branches; schema-closed does not mean correct—a wrong in-schema pick still ships if you skip the threshold.
Jev's slot isn't a second brain. It's the inner-loop judgment: state in, typed answers out, code owns the rest. LLM still writes. Jev branches. Schema-closed ≠ correct — a wrong in-schema pick still ships if you skip the threshold.
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Pricing, evaluations, and the name Jev
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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.
