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

來源原標題

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

作者

Simon Willison

原始標籤

blog

原始連結

https://simonwillison.net/2026/Sep/21/jev/

摘要原文

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: 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. The Jev 1.13 jaggedness documentation offers useful guidance as to Jev’s strengths and weaknesses. It’s currently not great with numbers, dates, or “adversarial content”. 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. 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. 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: 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. Update 22nd September 2026 : I released llm-typesafe , a plugin that adds support for Jev to my LLM CLI tool and Python library. Basic usage looks like this: See the README for examples of other query types. This is Jev introduces a new shape of LLM—System One, aka Decision Models by Simon Willison, posted on 21st September 2026 . Next: Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war Previous: Generating running routes with GPT-6 Astra and ChatGPT Work Sponsor me for $10/month and get a curated email digest of the month's most important LLM developments.