Decision Models

A decision model returns a typed answer, such as a choice from a fixed set, a probability or a score, instead of text. It replaces the step where code asks a chat model a question and parses the reply.

The shift

Until September 2026, code that needed a small judgment called a chat model. The prompt listed the allowed answers, the code parsed the reply, retried when it came back malformed, and set temperature to zero to keep it stable. Every answer cost output tokens and a full generation's latency.

A decision model takes the question and the allowed answers and returns one of them with a calibrated probability in a single pass. There is nothing to parse and little or no output to pay for. TypeSafe calls these "System One" models: fast judgments that sit inside ordinary code, where a chat model does the slower thinking. TypeSafe

Who ships them

When to use one

Use a decision model when the answer set is known before the question is asked:

  • classifying an input or a document
  • routing a request to a handler, a model or a person
  • choosing an agent's next action from a fixed list
  • scoring against a rubric or gating a risky command
  • matching, deduplicating and ranking

Keep a chat model for anything that must produce text, code or an open-ended answer. In an agent, the loop runs on a chat model and the small decisions inside it can move to a decision model. When the output is structured data rather than a choice, private page are still the tool.

Which skill runs it

The typesafe-ai skill builds features on TypeSafe's models. Read the vendor's latest docs before choosing, because pricing and speed figures so far come mostly from the vendors.


Timeline