Jev 是什么：不生成文本的 System One 决策模型
ChatGPT 联合创始人的新模型 Jev 用 Choice/Score 类型化提问返回校准概率，$0.042/百万 token、70–500ms，主打把 90% 的分类调用从 System 2 价格降到 System 1。
200x faster. 400x cheaper. And it can't hallucinate - because the answer space is defined before it even runs The chatgpt co-creator left OpenAI, spent 2 years in silence, and built a model that doesn't generate text at all Jev is a "System One" model. your code asks it typed questions - Choice, Score, Noul - and it returns calibrated probabilities for every option in one parallel pass. no tokens. no parsing. no retry loops $0.042 per million input tokens. output included free. 70 to 500ms latency The name references Jevons paradox: when intelligence gets this cheap, you don't optimize the same use cases. you automate an entire class of decisions that were never worth the API call before Normal LLMs generate your answer token by token, then you pray the string is valid. Jev generates probabilities for each option in parallel - the answer space is defined before it even runs 90% of production LLM calls are classification. we've been paying System 2 prices for System 1 work this whole time
这条是引用着另一条写的：x.com/0xaigoat/status/2102027640752177195