Jev introduces a new shape of LLM - System One, aka Decision Models
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Ap…
Marked important, but published more than 30 days ago, so it moved down to this band.
On this page12 sections
The editors' notes below — why it matters, the context, the learning path — are written in Chinese.
Record
The signal
#Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores. TypeSafe describe Jev like this: Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. It's also very fast, and really cheap. Regular LLMs are priced in terms of input and output tokens, with output generally charged at significantly higher rates. Jev charges only for input - output is free - and the input price of their first model is $0.042 per million tokens - cheaper even than OpenAI's GPT-5 Nano ($0.05/million). Jev lets you ask questions about text or semi-structured data. You compose a "state" object containing a string, array of strings, or set of name-value pairs - this might describe an article, or a customer, or any other kind of record. You then send that to their API with one or more questions, and get a reply b…
Why now
Why it matters
#大量生产里的模型调用其实是在做判断——这是不是垃圾、该交给谁、先处理哪条——却用生成式模型逐 token 写出 JSON。Jev 把这类判断做成了只返回概率的专用接口，价格和延迟都掉了一两个数量级；同时它把「为什么」彻底拿掉了，这让评测从可选变成了必需。
Background
Technical context
#「System One」取自卡尼曼的快思考与慢思考：快速、直觉式的判断对应系统一，缓慢审慎的推理对应系统二。技术上，生成式模型做分类时要逐个 token 写出字段名、标点和解释；只输出预定义选项上的概率，可以省掉解码、并行求值多个问题，这是它在速度和成本上占优的主要来源。
Audience
Who should care
#Next
Learning path
#- 先读《模型选型与约束匹配》：有一类任务大模型是形状不对，而不只是贵
- 把你流程里所有「让大模型输出一个选项」的调用列出来，数一数有多少
- 挑其中一个，用一小批有标注的样本量它的准确率与置信度校准
- 读《评估闭环》：输出只剩一个数字时，唯一能发现偏见的办法是结构化实验
- 把它与本站的 LFM2.5-Encoders 并排读：本机小编码器与托管判断接口，各自的取舍
Network
Where it sits in the network
#How this signal connects to neighbouring technologies, skills and background knowledge. Follow any link to keep exploring.
Further thought
Follow-up questions
#- 你的流程里有多少次大模型调用其实只是在做一个多选一？
- 一个只返回概率的模型出错时，你用什么办法发现它错在哪里？
- 厂商自制评测的参考标签由另两个模型生成，这样的评测能说明什么、不能说明什么？
Publication record
Featured in
#The briefings that carried this signal, newest first; the last row is the weekly review of its week. The tag is the section it ran in.
技术简报 - 2026-09-24
Worth trackingWeekly review · Sep 21 – Sep 27
Worth tracking
Sources