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: Handing Judgment to Code, Not Chat
Jev doesn't generate text; it takes a state and typed questions (choices, scales, yes/no probabilities) and returns code-ready if values in tens to hundreds of milliseconds, in parallel.
Jev不是更会说话的大模型，而是把「判断」从聊天里抽出来、直接交给代码用的模型。 普通LLM要先把答案写成一段话，你再解析、校验、祈祷格式别崩。 Jev不写一个字：给它一段状态，再丢几个带类型的问题——从选项里选、按量表打分、给是/否概率——几十到几百毫秒一次并行答完，输出就是代码能if的值。
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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.
