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 counting bottleneck: probabilistic output struggles with definite answers
A developer built a counting app with Jev and found it returns probabilities instead of definite answers, often failing obvious questions, exposing a bottleneck in exact counting.
here's one of the major bottlenecks of Jev... i made an app that uses Jev to tell me how many a's are in a word it doesn't return a clear answer, it returns probabilities, it's rarely 100% sure it's right most of the time, but it also gets such an obvious question wrong
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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.
