OpenAI’s Decisions API is fast and cheap—but what is it deciding?
The generally available service turns text, images or JSON into a probability, fixed choice or rubric score. Its low price could put model judgements inside routine workflows, while the evidence so far comes from OpenAI and early customers.
The short version
- What happened
- OpenAI says its Decisions API is now generally available and returns one of three bounded answer types: a probability, a choice from supplied options or a score against a rubric.
- Who it affects
- People may experience this service indirectly when software routes a support request, flags a document or chooses an agent’s next step. Faster automation can shorten waits, but a cheap misclassification can also be repeated at scale unless a person can review and correct it.
- What remains unproven
- OpenAI supplies both central sources; there is no independent benchmark or audit in the release.
Keep in mind
OpenAI supplies both central sources; there is no independent benchmark or audit in the release.
Customer examples use different tasks and disclose uneven denominators, so their speed, cost and accuracy claims cannot be compared as one study.
The ten-times-faster figure is an OpenAI comparison with GPT-6 Luna through its own Responses API, not a cross-provider result.
A bounded answer does not prove calibration, fairness or safety for any consequential use. Performance must be tested on the actual population, workflow and failure costs.
Source analysis · 2 linked sources. Primary analysed source date: 22 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
A smaller answer surface is the product
OpenAI made its Decisions API generally available on 22 October, offering developers a specialised route for repeated, bounded judgements such as classifying an input, choosing a next action or scoring against a rubric. Instead of writing a free-form response, the service returns a probability, one of the options supplied by the developer, or a probability-weighted score. It accepts text, images and native JSON, and can answer as many as 400 questions in one request.[1][2]
Cheap, fast calls can move models into the workflow’s hot path
The endpoint currently supports GPT-6 Luna and costs $0.10 per million input tokens, with cached input at $0.01 and no output-token fee. OpenAI says it is about ten times faster than using Luna through the Responses API. Early customers report faster routing, risk scoring and conversational actions, but their comparisons use different tasks and denominators. Box’s disclosed test covered 30 insurance-certificate cases; other examples do not publish a sample size or error distribution.[1][2]
The distinction that matters
Three answer types, three kinds of downstream control
The developer defines the question and what the application does with the returned value.
Predicate
Estimate whether a condition is true
Returns a probability from 0 to 1
Choice
Select from developer-supplied options
Returns a choice, probabilities and confidence
Score
Rate against ordered rubric levels
Returns a probability-weighted score
A confidence number is not a decision policy
Applications must still choose the question, allowed answers, threshold and action that follows. That matters when a score routes a customer, flags a payment, prioritises a worker’s case or lets an automated agent continue. The service’s narrow output can make integration easier, but it does not establish that the underlying judgement is fair, calibrated or safe for a particular population. Teams need representative tests, abstention and escalation rules, monitoring for drift and a way to reverse harmful outcomes.[1][2]