Franklin AI News Brief

OpenAI’s Decisions API reaches general availability for bounded application choices

Key Takeaways

  • The endpoint supports probability, choice and score outputs, with native JSON, larger question batches and a GPT-Live integration.
  • OpenAI has made the Decisions API generally available for applications that need focused answers from a defined set of possibilities.
  • The October 22 announcement positions the endpoint around classification, routing and other recurring decisions, using GPT-6 Luna rather than a free-form conversational response.
  • Define the decision before sending context In OpenAI’s announcement, developers specify a question and its allowed answers, then supply text, images or native JSON.
  • The API supports three answer types: a probability, a choice or a score.

OpenAI has made the Decisions API generally available for applications that need focused answers from a defined set of possibilities. The October 22 announcement positions the endpoint around classification, routing and other recurring decisions, using GPT-6 Luna rather than a free-form conversational response.

Define the decision before sending context

In OpenAI’s announcement, developers specify a question and its allowed answers, then supply text, images or native JSON. The API supports three answer types: a probability, a choice or a score.

That structure makes the application’s decision boundary explicit. A routing task, for example, needs a defined set of destinations and examples that distinguish them. A constrained output shape can simplify integration, while the application still needs to evaluate whether the selected answer is appropriate.

The release adds context and batching options

OpenAI says the generally available version includes prompt caching, inference residency in the US and EU and support for up to 400 questions per request, twice the previous limit. It also allows images within question instructions.

The company advertises decisions up to ten times faster than GPT-6 Luna through the Responses API. That is a vendor comparison attached to this focused endpoint, rather than a promise that every application becomes ten times faster. Overall latency still includes the surrounding workflow and the work performed after the decision.

Voice integration needs its own evaluation

The announcement includes native integration with GPT-Live so an application can make decisions during a conversation. The useful question is whether the selected action fits the context available at that moment, especially when the next step is clarification or escalation.

For a first evaluation, choose one frequent decision and collect examples of both ordinary and ambiguous inputs. Compare the result with the intended action and define how uncertainty is handled. A fast bounded answer is most useful when the application has a clear policy for what follows it.

Our read

Franklin AI Take

The endpoint is most useful when the decision itself is well defined. Start with a recurring choice, examples that distinguish its options and a clear fallback for ambiguous inputs. OpenAI’s larger batches and voice integration broaden where that pattern can be used, while faster inference does not remove the need to check the selected action.