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. 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.