OpenAI made its Decisions API generally available on October 22, 2026, the company announced, taking the GPT-6 Luna–powered endpoint out of public beta at a price of $0.10 per million input tokens. The API returns bounded, typed answers (a probability, a selection, or a rubric score) that applications use to classify content, route requests, and prioritize work.
OpenAI said the API focuses Luna’s intelligence on questions with bounded answers, enabling near real-time decisions from text, images, and structured inputs. The company said the endpoint delivers those answers up to 10x faster than asking GPT-6 Luna through the Responses API.
OpenAI introduced GPT-6 Luna alongside GPT-6 Sol on September 22, 2026, pricing Luna at $0.10 per million input tokens and $0.50 per million output tokens. The company said it cut API prices for both models by 50% compared with their GPT-5.6 promotional pricing and described the pair as advancing the frontier on cost efficiency; it continues to describe GPT-6 Astra as its best model across the board. In the API, the two models are available as gpt-6-sol and gpt-6-luna.
How the Decisions API Works
Requests go to POST /v1/decisions, and gpt-6-luna is currently the only supported model, according to OpenAI’s developer documentation. Each request pairs shared input (a text string or an ordered array of messages, tool calls, and tool outputs) with between 1 and 400 questions, and the response contains an answers array that echoes each question’s developer-assigned name.
The API supports three question types. A predicate question returns a probability from 0 to 1 that a condition is true, such as visible damage in a product photo. A choice question selects one of the developer’s predefined options and also returns probabilities for each option and a confidence value. A score question rates an input against two to ten ordered rubric levels and returns the probability-weighted average of the level indices, so a score can fall between levels.
Developers can pass structured data directly through an input_json content part in a user message, and images can be supplied as inline base64 data URLs or as publicly accessible HTTP or HTTPS URLs. Because conversation history can be supplied as input, the API can evaluate a request in context alongside function and custom tool calls and their outputs. The documentation states that files, audio, and item references are not supported, and the endpoint does not accept file_id inputs.
For decisions that depend on an earlier answer, the documentation directs developers to send separate requests. It also points builders to Structured Outputs or function calling in the Responses API when they need a custom JSON object or a model-requested tool call rather than one of the three typed answers.
Additions Since the Public Beta
OpenAI said that after the public beta it added a native GPT-Live integration, prompt caching for reused context, and inference residency in the United States and EU, plus additional inference capacity in several regions in Asia. Developers can now pass native JSON inputs, include images in question instructions, and ask up to 400 questions per request, twice the previous limit.
The documentation’s what’s-new list further includes hosted HTTP and HTTPS image inputs, conversation-history evaluation with tool calls and outputs, organization usage reporting, and Private Link support. The usage endpoint, GET /v1/organization/usage/decisions, requires an admin API key with the api.usage.read permission and returns token usage and request counts that can be filtered or grouped by project, API key, or model.
Under prompt caching, repeated requests can reuse a matching prefix of shared input at least 1,024 tokens long and receive discounted cache-read pricing; question content, including question-specific images, sits outside the reusable prefix. The API places an implicit cache breakpoint at the end of shared input by default, and developers can partition reuse with a caller-provided prompt_cache_key or switch to explicitly marked breakpoints.
With the GPT-Live integration, a voice application can run decisions as input or output transcripts arrive, and Live emits a decision event for the application to act on. OpenAI’s example configuration, added to session.triggers, selects an avatar’s facial expression from transcript updates, and the company describes a customer support agent using the same pattern to decide whether to ask a clarifying question, route a request, or escalate an issue.
Reported Use and Early Evaluations
OpenAI said its own teams already apply the API across product and business workflows: the dots team uses it to reduce response times and improve interactivity, the Personal Finance team uses it to categorize transaction descriptions, and the Communications team uses it to classify coverage by theme and score whether the coverage reinforces or challenges key narratives, helping identify what warrants closer review.
Customers quoted in the announcement reported speed and cost gains in their own evaluations. Box’s vice president of product management for platform and integrations, Biswaroop Palit, said the Decisions API combined with Box Extract delivered roughly 5x faster route selection across 30 certificate-of-insurance cases at the same accuracy as the Responses API, sending lower-confidence cases to reviewers. Datadog staff engineer AJ Stuyvenberg said the API scored the risk of agent-generated rshell commands before execution, delivering 40x faster p95 latency than available frontier models with comparable accuracy. Ramp research engineer René Sultan said the API made attributing AI costs to the underlying work 4x faster and 30% cheaper than using GPT-6 Luna, without sacrificing accuracy.
Sierra engineer Ajeet Grewal said the API chooses the next action during parts of Sierra’s orchestration in live customer conversations at under 280 milliseconds p95, about 3x faster and 90% lower cost per call than the company’s previous approach, with no drop in customer outcomes. Speak CTO Andrew Hsu said the Decisions API integration in GPT-Live reduced time to next action by more than 90% in the company’s testing, from 1.4 seconds to 0.13 seconds.
Pricing and Data Controls
The Decisions API costs $0.10 per million input tokens and $0.01 per million cached input tokens, and OpenAI charges only for input tokens, with no output-token charges. Regional processing premiums and long-context input pricing multipliers apply, and cache-read and cache-write rates match the standard gpt-6-luna prompt caching rates.
Regional processing is supported in the United States and Europe (the EEA plus Switzerland), data residency is available in all supported API regions, and Zero Data Retention and HIPAA use are available to eligible customers. The API is accessible through the dedicated Decisions endpoint and a Decisions Playground on OpenAI’s platform, where developers can test questions against their own text or images before writing code.
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