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 and Local Decisioning Models: Prioritizing Latency
The author argues that running decisioning models locally is the winning approach, noting they can be small enough for most hardware and that latency should be optimized to maximize decisions per second.
Jev opened the box, but I believe that having your decisioning model run locally is the winning recipe. Decisioning models can certainly be made small enough for most hardware and you really want to maximize for latency with these models, as a higher decisions-per-second may be
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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.
