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.
The pattern that works with Jev: classify first, generate second
A user shares a Jev practice: classify first, generate second, applying the same idea to Reddit thread triage and cutting pipeline cost by ~30x, noting the router now builds this in.
this is the pattern that actually works with jev: classify first, generate second. we do the same thing one layer up for reddit threads (cited by ai / not cited / worth replying), and it cut our pipeline cost ~30x. nice to see it built into the router
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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.
