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 suits CPU behavior that occasionally errs like humans
The author argues Jev fits simulating CPU behavior that occasionally errs like a human, since LLMs are slow and programs or RL always take the optimal path.
思ったけどJevってこういうのを開発中に人間っぽくたまにミスるCPU動作に向いてるよな🤔 LLMだと遅いし、プログラム(オートマトン)や強化学習だと最適解で動いてしまう
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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.
