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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsOpenAI released its Decisions API in beta on October 6, 2026, with gpt-6-luna as the supported model. The endpoint, /v1/decisions, is designed to turn text and images into typed outputs such as truth estimates, fixed-option choices, and rubric-style scores. OpenAI says it is available to all developers in public beta; the API’s current reference should be checked for implementation details that are not established in the release announcement.
What the Decisions API does
Rather than asking a model for an open-ended response and then extracting a decision from the prose, the Decisions API is presented as a way to request a defined decision type. OpenAI describes inputs as text or images and identifies three output forms: predicates, choices, and scores. That makes the endpoint relevant to applications that need a structured signal for routing, classification, or evaluation.
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OpenAI’s integration guide illustrates routing patterns, including deciding whether a slide presenter should advance and sending a complex comparison to a reasoning model. These are examples of possible application logic, not evidence that the API will make a particular decision accurately in every setting.
Predicates: estimate whether a statement is true
A predicate estimates the probability that a statement is true. This can help an application express a check as a yes-or-no proposition with an associated estimate, rather than relying on a free-form explanation. The estimate is still a model output; it should not be treated as independently verified fact.
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Choices: select from predefined options
A choice selects among options supplied by the application and returns confidence scores. This suits decisions with a fixed set of possible outcomes, such as selecting a route or action. The application defines the choice set, so this is not the same as asking the model to invent an unrestricted answer.
Scores: evaluate against a numeric range
A score evaluates an input against a numeric range, such as a rubric. The range and rubric help make the expected output explicit, but a numeric score does not by itself establish that an evaluation is calibrated or consistent for a particular use case.
Release status, model, and endpoint
OpenAI’s API changelog records the beta release on October 6, 2026, names gpt-6-luna, and says the API turns text and images into typed answers. The OpenAI Developer Community announcement posted the same day says it is available to all developers in public beta. OpenAI’s documentation also demonstrates a request to /v1/decisions using gpt-6-luna.
The data-controls documentation lists the endpoint and model and says the endpoint is available in all supported API regions. It separately identifies regional processing in the United States and Europe (EEA + Switzerland). “Available in all supported API regions” should not be read as a claim that regional processing is available worldwide; developers with residency or processing-location requirements should confirm the specific region and account terms in OpenAI’s data controls documentation.
How it compares with using the Responses API
The practical distinction is the shape of the task. Decisions is presented for typed predicates, choices, or scores. The broader Responses API can be used to build decision logic with more open-ended generation, but the supplied official materials do not establish a complete feature-by-feature comparison. The right choice depends on whether the application needs a constrained decision output, what validation it must perform, and the integration already in place.
| Consideration | Decisions API | Responses API approach |
|---|---|---|
| Output style | Typed predicate, predefined choice, or numeric score, as described by OpenAI. | Can be used to build broader generation and decision logic; a complete comparative schema is not stated in the cited materials. |
| Latency claim | OpenAI’s changelog says typed answers are “10x faster than the Responses API.” | The comparison’s benchmark conditions are not stated in the cited materials. |
| Pricing and limits | Current pricing and rate limits are not stated in the cited materials. | A directly comparable price and limit comparison is not stated in the cited materials. |
| Best fit | Applications needing a defined decision type and constrained output. | Applications needing a broader or more open-ended response workflow. |
The speed figure is an OpenAI claim, not an independently verified benchmark. The changelog compares the endpoint with the Responses API; the community announcement instead describes it as “up to 10x faster than GPT-6 Luna through the Responses API.” Neither source gives test conditions, workload, or measurement methodology, so the figure should not be treated as a universal latency guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What developers should verify before building on it
The release and overview establish the endpoint, model, broad input types, and decision categories, but they are not a complete implementation specification. Before deploying an integration, consult the live Decisions API reference and confirm the details that apply to the account and use case:
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- Current pricing and how Decisions usage is billed.
- Rate limits and any account-specific usage tiers.
- The complete request and response schema, including error behavior.
- Supported input constraints for text and image requests.
- Region availability and whether the required regional processing location is supported.
- Whether OpenAI has published methodology or evaluation conditions for the speed comparison.
For production use, validate outputs against the application’s own requirements and decide how the system should handle low confidence, invalid responses, or decisions that need human review. A typed result can simplify downstream handling; it does not remove the need to assess the consequences of an incorrect classification or route.
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