CommunityJev Guides & Articles
OpenRouter: what Jev returns
Kenny Rogers explains Jev as a decision model and shows Choice, Score, and Noul responses from calls he placed through OpenRouter on 21 September 2026.
- Format
- Article
- Published by
- Community
- Author
- Kenny Rogers
- Last verified
- Oct 2026
Used inJev for Software & DevTools
- Platforms
- OpenRouter
Highlights
- Dated 21 September 2026. The sample calls use typesafe/jev-1.13 through OpenRouter's Decisions API.
- Says input is text only: a string, a JSON object, or an array of text. Output is a typed answer plus a distribution.
- Lists the OpenRouter price as $0.042 per million input tokens, with output free.
- Points at a companion post, already listed, for when to keep an LLM instead.
Watch out
The calibration paragraph is his summary of TypeSafe's claim. A single answer can still be wrong, which the post says outright.
Questions about this entry
Short answers drawn from the linked project or page.
No text output, which is the real difference. The captured page has the model take a passage plus a typed question and return one answer from a predefined set with probabilities for the others, plus an overall confidence figure. Whether that earns its keep against a cheap classifier is a question this post does not test.
The calibration discussion is the author's summary of TypeSafe's claim, and the entry copy says so. The post's own honest note is that a single answer can still be wrong, so treat the distribution as decision input and set your own thresholds.
The captured page says input is text only, accepting a string, a JSON object, or an array of text, and that output is a typed answer plus a distribution. Structured state is allowed; images and binary payloads are not in that list.
The entry copy lists the OpenRouter price as a low per-million input token rate with output free, which is the shape of a decision call rather than a generation call. That is OpenRouter's list price, not a measured average, and the model id used is the 1.13 line.
Choice picks one option from a fixed set and returns the selection, a probability for every option, and a confidence; Noul returns a yes-probability; Score places content on ordered levels you describe. One request carries a state plus a questions object, and Jev answers every question in a single pass under the key you gave.
POST to https://openrouter.ai/api/alpha/decisions with the model typesafe/jev-1.13, a state string, and a questions object. The captured examples were real calls placed on 21 September 2026 and run as written once OPENROUTER_API_KEY is set in the environment.
A System One model takes in a piece of state and returns a decision as a typed value you defined in advance, plus an actual probability from zero to one. TypeSafe names the class after Kahneman's System One: fast, pattern-matching thinking, as opposed to slow deliberation.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: Simon Willison's overview of Noul, Choice, and Score, and where a decision model fits.
benwerd53 pointsJev introduces a new shape of LLMRead the thread on Hacker NewsHacker News: swyx posts the Latent Space interview with Diogo Almeida.
swyx4 pointsJev: System One Models for Prod, Not God – With Diogo Almeida, CEO, TypeSafe AIRead the thread on Hacker NewsX: Diogo Almeida in five minutes
X: LLMs vs. Jev, clearly explained
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