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 vs. LLM as Judge, clearly explained
Using a support refund example, compares Jev and LLM as evaluators to judge whether AI answers are grounded, honest, and useful.
Jev vs. LLM as Judge, clearly explained. Imagine a support agent says, “Done. I issued your refund.” The trace shows that the agent looked up the order, but never completed the refund. An evaluator now needs to decide whether the answer is grounded, honest, and useful. Both an
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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.
