Jev and the decision-model wave, explained — and how to run one on your own machine
Written by Jakub Rusinowski · Published September 30, 2026
TypeSafe's Jev answers typed questions with probabilities instead of text. How decision models work, what the benchmarks really say, and which open ones you can run locally.
- Two weeks that created a model category
- Why "System One" and why "Jev"
- How a decision model works
- Anatomy of a request
- What decision models are good for
- What the benchmarks actually say
- The open reproduction wave
- Calibration: the part the marketing skips
Decision models are language models that never write. You give one some text and a list of typed
questions; it gives back a probability for every allowed answer, all at once, in well under a second.
TypeSafe AI launched the idea as a product called Jev on 15 September 2026. Two weeks later there are
about 70 open reproductions, and since Ollama 0.35 you can run three of them locally with one
command. This post explains how they work, what the benchmarks do and don't show, and where they
fit next to the chat models you already run.
If you only want to get one running, skip to
the setup guide.
-…
Frequently asked questions
What is a Jev-style decision model?
A model that takes a state and typed questions and returns a probability for every allowed answer in one scoring pass, without generating text. "Jev-style" refers to the interface TypeSafe AI introduced with Jev; the open models reproduce the interface, not Jev itself.
Is Jev open source or available in Ollama?
No. Jev is TypeSafe AI's hosted model with closed weights. Ollama runs open models — Nimble and Tev1 — that use the same request format, so code written for one works with the other.
Is a decision model the same as a classifier?
Close. A classic classifier is trained for one fixed label set. A decision model takes new questions and new options at request time, described in plain language, so one model handles routing, moderation and scoring without retraining.
How is this different from structured outputs or JSON mode?
JSON mode constrains a chat model's generated text to a schema, but you still get one sampled answer. A decision model returns a probability for every option at once, answers many questions per request, and doesn't generate at all — which is also why it's faster.
What hardware do I need?
The smallest one, Tev1 0.8B, is an 812 MB download and runs on a CPU. Nimble 9B is 9.5 GB. The strongest open model, AutoJev-27B, needs a workstation-class GPU. Check your machine in the analyzer. --- Sources: TypeSafe AI launch post and documentation (typesafe.ai, docs.typesafe.ai); Ollama blog, 29 September 2026, and API reference (docs.ollama.com/api/systemone); Bespoke Labs Nimble repository and public benchmarks; Jev Decision Index 0.2.1 data (Hugging Face Space multimodalart/jev-decision-index, generated 28 September 2026); Hugging Face model cards read 30 September 2026. The Decision Index is community-maintained and not affiliated with TypeSafe AI.
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