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Jev：只输出概率的决策模型

Simon Willison · 2026-09-21 · 原题《Jev introduces a new shape of LLM - System One, aka Decision Models》 · 原文约 4 分钟 · 阅读原文 ↗

TypeSafe AI 的 Jev 代表一类新模型：输入文本，输出浮点数概率而非文本，速度快、极便宜，适合分类、排序等决策任务，但让 AI 更加黑箱化。

论证要点

Jev 接受文本或半结构化 state 输入，输出对应类别的浮点数、置信度与概率分布，而非文本。
支持三类问题：Noul（是/否，输出 0-1 置信度）、Choice（从选项中选择并给出分布）、Score（在数值区间上打分）。
定价只收输入费，输出免费，首个模型输入价 $0.042/百万 token，比 GPT-5 Nano 的 $0.05 还便宜，且问题并行评估。
作者认为决策模型框架适合分类任务，如垃圾邮件检测、打标签、优先级排序，也用于搜索重排：BM25 召回 100 条后由 Jev 打分。
作者担忧 Jev 是向黑箱机器学习的进一步倒退：只返回浮点数，无法解释哪些内容信号导致该判断，偏见问题更需警惕。
社区已出现 jevchat、jev-leftpad、jev-2048 等创意用法，以及基于 Qwen 3.5 的开源复刻 Kev 和 JevBench 基准。

对实践的启示

做 AI 应用的人可以把 Jev 类模型当作廉价、高速的分类与打分组件，嵌入检索重排、审核、排序等环节；但因其不可解释，必须用大量结构化评测来验证行为，避免在招聘等高风险场景直接使用。

作者承认的反方与局限

作者承认 LLM 本身就是黑箱，但 Jev 连要求模型解释决策的机会都没有；他特别担心有人用 Jev 给求职者排名，因为浮点数可能掩盖模型中的偏见，且实验性地拆解这种偏见会很困难。

原文摘引

Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.

Jev doesn’t even give you that: put in all the text you want, the only thing you’re going to get back is a floating point number.

I really hope nobody uses Jev to rank job applicants—that floating point number could conceal all manner of unseen bias baked into the models

▸ 展开原文全文（32 段，抓取自 Simon Willison）

Jev introduces a new shape of LLM—System One, aka Decision Models

21st September 2026

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 back for each.

You can ask three kinds of questions:

- Yes/No questions, which Jev calls “Noul” questions—their CEO confirmed on Hacker News that this is short for Bernoulli, from the Bernoulli distribution. You pose a statement and get back a floating point number between 0 and 1 for how confident the model is that the statement is true.

- Choice questions, where the model picks one from a set of provided options—actually a confidence score plus a probability distribution across all of the options.

- Score questions, where you provide sequence of numeric levels with descriptions and it provides a floating point score somewhere along that range.

The Jev API can accept a single document (“state”) and as many questions as you can cram into the context window. Questions are evaluated in parallel, so sending many questions should take a similar time to sending just one.

I think the decision model framing is useful for understanding where to use Jev. It’s great for anything that can be expressed as a classification task—think spam detection, suggesting labels, prioritization and ranking.

I’ve also been experimenting with it for search reranking, where you fetch 100 likely matches using an inexpensive algorithm like BM25, then have Jev score those 100 candidates for relevance against the original query.

Black boxes are back in fashion

Something I’ve found a little uncomfortable about Jev is how it very much represents a regression even further towards black box machine learning systems.

LLMs are black boxes already—you can ask them to justify their decisions, but you can’t guarantee that what they say is useful or accurate.

Jev doesn’t even give you that: put in all the text you want, the only thing you’re going to get back is a floating point number. If Jev marks something as spam, which content signals tipped it off?

This also means that concerns about bias should be front and center. I really hope nobody uses Jev to rank job applicants—that floating point number could conceal all manner of unseen bias baked into the models, and experimentally picking that bias apart is going to be a tricky business.

(I tried one experiment where I had Jev score every city in the San Francisco Bay Area on a yes/no answer to whether they were a “Good city?”—it rated Cupertino top and East Palo Alto bottom. Huh.)

In practice, this all means that evals and structured experiments are even more important than they are for regular LLM projects. Thankfully, Jev is so cheap that running hundreds or even thousands of experimental prompts through it costs just a few cents.

Unconventional uses for Jev

It’s been really fun watching the wider community come up with potential use-cases for Jev over the past few days. Here are some creative ones that caught my eye:

- jevchat by Kyle Pena turns Jev into a (terrible) chat model. “At every step it asks Jev one question: Given the user’s question and the reply written so far, which symbol comes next?”. ericpruitt on Hacker News: “It’s the digital equivalent of Morty speaking with the death crystal”.

- jev-leftpad by Fatih Kadir Akın implements left-pad with the prompt “How many spaces are needed before value to reach targetLength?” and a choice query allowing options from “0 spaces are needed” to “10 spaces are needed”.

- jev-2048 by Andy Gayton uses Jev to play the 2048 sliding puzzle game.

Open weight recreations

There’s also been a flurry of projects attempting to create a model like Jev using on top of open weight models. Kev is one interesting example, using Qwen 3.5 to produce 0.8B, 4B, and 9B models. Here’s the accompanying Hacker News thread, where someone linked to a JevBench benchmark that has already cropped up to compare “Jev-class decision models”.

Given Jev was released just under a week ago, the amount of activity around it is extremely impressive.