A System One model is an AI model that returns typed answers and calibrated probabilities instead of generated text. You give it content and typed questions; it returns the selected option, a probability for every option, and a confidence value. Jev, from TypeSafe AI, was the first one, announced on 15 September 2026.
720 examples18 use cases8 recipes2 models
Every System One call is a state plus questions. A question is one of three shapes, and each returns a number you can branch on.
Every model announced as a System One model, with its prices, limits and sources.
Jev 1.13 is the first System One model. It reads text you supply, answers typed questions about it with calibrated probabilities, and returns no generated prose. TypeSafe AI shipped it on 15 September 2026 as a closed managed API.
Laya is an open-weights System One model from Convai Innovations. It answers typed Choice, Score and Noul questions about text you supply, returns probabilities instead of prose, and runs on your own GPU or CPU under Apache 2.0.
More vendors will appear here as they ship System One models. Know one? Submit it.
Projects, tools, cookbooks and write-ups from people building on these models.
Browser Use's fast browser agent where Jev picks the operation and the DOM element in a single request; a small LLM is only invoked to write text when typing is required. Demoed on a Google Flights search.
Attempt to run a Jev-style System One decision model locally on a single RTX 3090. Companion site at openjev.com. The repository was renamed to TheoLeeCJ/SemIf on GitHub, and the old openjev URL now redirects to it.
Open reimplementation that trains a small model to choose among a changing list of text options, emitting one probability per option in a single forward pass. Ships Doom, chess and Wikispeedia demos.
Makes one AI trade decision every Monad block on the Kuru MON-USDC market using Jev. Live at jev-trader.vercel.app.
Independent community collection of use cases, patterns, prompts and starter code for Jev, explicitly not official. The 2023 creation date means the repo was renamed from an older project, so its 412 stars are not Jev-era traction.
Official agent skill for Claude Code, Codex and other agent environments; installs via claude plugin marketplace add typesafe-ai/skills or npx skills add typesafe-ai/skills. MIT licensed.
Proof-of-concept MCP server for Jev that lets Claude Code, Claude Desktop and Codex call the model and receive probabilities they can branch on. The most-starred Jev MCP server found.
Walks through the Choice, Score and Noul primitives live in the TypeSafe playground, with speed and cost numbers and their caveats. The most-watched Jev video found.
The patterns people reach for, grouped by the job they do.
Decide where a piece of work goes next and whether your code is allowed to act on it without a person.
Judge whether retrieved text is relevant, ranked correctly, or actually supports the claim it is cited for.
Screen inputs and outputs for injected instructions, policy breaches, and answers that disagree with themselves.
Turn messy text into typed fields, scores, and labels that downstream systems and models can use directly.
Pick the next move fast enough for a loop that is already running, whether that loop is a game, a robot, or an agent.
Code you can paste, with the thresholds left in your code.
An LLM cites a document for a claim. This recipe checks the citation in two steps: a plain string match that catches quotes missing from the source, then one noul question that returns the probability the quoted section actually supports the claim. Your code turns that probability into a verdict.
A single Choice question sorts an inbound support ticket into one of four queues and returns a probability for every option. The model supplies the label and the certainty; your router applies the thresholds, holds the doubtful tickets for a human, and keeps every side effect in your code rather than in the prompt.
A support ticket needs a category, a severity, and two yes/no facts that only matter for one category each. Asking in sequence costs four round trips and four copies of the ticket. This recipe sends all four questions in one call, including the speculative ones, and lets the routing code ignore what it does not need.
One Choice names the action the user is asking for and one Noul says whether the message confirms it. Two numbers come back, and a table of per-action thresholds in your code decides the rest: a read runs at moderate confidence, a refund needs more, and anything below the floor goes to a person.
Start here if the category is new to you.
A System One model returns typed, calibrated decisions instead of text. What that means, where the name comes from, and how Jev fits.
Updated
Jev is TypeSafe AI's decision model. It answers typed questions about text with calibrated probabilities, and it is not an LLM, because it writes nothing.
Updated
System One models return typed decisions; LLMs return text. A side-by-side on output, latency, cost, hallucination and when each one wins.
Updated
Engineers called Jev a zero-shot classifier within hours of launch. What the comparison gets right, what it misses, and what is actually new.
Updated
The eight most recent entries in the directory.
Send the link. Projects, tools, cookbooks, videos, write-ups and threads all count. Every entry is checked and credited to its author.