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.
Comparing Jev's Noul/Score/Choice abstraction with autorubric criterion types
The author investigated TypeSafe AI's Jev and found it uses an abstraction similar to autorubric, released 8 months earlier, noting their criterion types map 1:1 to Jev's Noul/Score/Choice.
I spent some time investigating @typesafeai's Jev and striking to see them use a similar abstraction as autorubric -- released 8 months ago and to be presented at @COLM_conf next month. Our criterion types map 1:1 to Jev's Noul/Score/Choice: https://arxiv.org/abs/2603.00077
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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.
