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The Decisions API evaluates one input against a list of typed questions and returns a probability-backed answer for each. No text is generated. Instead the model returns a probability on each question, in one round trip. Use it to quickly classify content, route requests, gate actions, and score against rubrics. The decision models come in three sizes — decision-4b, decision-9b, and decision-27b — all reachable through the same OpenAI-compatible /v1/decisions endpoint.

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Anatomy of a request

A request has three parts:
  • model — which decision model answers, for example decision-9b
  • input — the text the questions are about
  • questions — up to 64 of them, all asked about the same input
Each question has a name (an identifier you choose, echoed back in the answer), instructions (the question itself), and a type. Types are built into the model so it reports its probability over the type’s fixed set of answers. Use choice for unordered categories (departments, topics) and score for ordered ones (severity, urgency, quality). A score of 1.4 over levels ["routine", "elevated", "critical"] reads as “elevated, leaning critical.”

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Example

The response echoes each question’s name:

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Working with the answers

Decisions are probabilities. You have to pick the threshold to interpret them. A moderation gate might block above 0.9, send 0.5–0.9 to human review, and pass the rest. These probabilities are one of the advantages over text output. choice and score answers carry a confidence, which is just a convenient way to extract the probability of the chosen answer.
Ask several questions in one request when they share the same input: the model reads the input once, so additional questions are nearly free.

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Choosing a model

All three sizes answer faster than generating text; they differ in accuracy and speed relative to each other. The sizes tend to agree on easy calls and diverge on hard ones. On the escalation example above, “should this be escalated?” gets 0.38 from the 4B, 0.66 from the 9B, and 0.89 from the 27B. The sizes also compose: for instance, you can run the decision-4b over everything and route uncertain answers — a predicate probability near 0.5, or a choice with no clear winner — to a larger model.