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: From Predicting Next Token to Predicting Answers
The Jev model predicts answers directly instead of the next token, supporting only three question types: yes/no, pick one of up to 255, and a custom scale of 2 to 10 levels.
okay, wtf is jev? in first principles, - normal LLMs predict the next token - jev predicts the answer three question types only. - yes/no - pick one of up to 255 - a scale you define, 2 to 10 levels follow for more #jev #typesafe
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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.
