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Which request settings does the Decisions API document?
OpenAI’s guide describes a POST /v1/decisions request with a model, shared input, and questions. The guide reviewed for this article lists gpt-6-luna as the available model. Model availability and API features can change, so check the live Decisions API guide before deployment.
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The main configuration choice is how to express each decision as a question type:
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- Predicate: estimates the probability that a condition is true. Use it for a clearly defined yes-or-no condition.
- Choice: selects among supplied options and returns a probability distribution plus a separate confidence field. Make options meaningfully distinct and include a fallback such as
otherif an input may not fit any named category. - Score: evaluates input against ordered levels. Define observable criteria for each level and distinguish adjacent levels. A returned score may be a probability-weighted average between level indices, rather than an integer category.
Keep each question focused on observable criteria. Questions about separate concerns should remain separate, even if they share the same input. Independent questions can be sent together in one request; if one decision depends on another decision’s result, use separate requests so the second can use that result.
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Which settings affect latency?
The reviewed guide does not document a Decisions-specific latency parameter, endpoint timeout, retry policy, or latency guarantee. Do not treat a question field or model choice as a documented latency control on this evidence. Measure end-to-end response times in your own client and deployment, where network conditions, SDK behavior, payload size, and traffic patterns all matter.
To compare request designs, use representative input sizes and concurrency, and record the response-time distribution rather than relying on a single result. Removing irrelevant input and asking only necessary questions are sensible ways to keep requests focused, but the guide does not quantify their effect on latency. Set timeout and retry behavior in the application using the documentation for your client and service environment; test transport errors and timeouts rather than assuming endpoint-specific defaults.
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How can you control Decisions API cost?
The OpenAI guide listed gpt-6-luna input pricing for /v1/decisions at $0.10 per 1 million input tokens when checked on October 7, 2026. It states that only input tokens are charged, with no cache-read, cache-write, or output-token charges. Regional processing premiums and long-context input pricing multipliers may apply. The page did not show a publication or update date, so confirm current applicable pricing before relying on this rate.
Cost-conscious request design is principally about the input: include context needed to answer, remove irrelevant material, and monitor token use on real requests. The guide does not establish a universal token budget or guarantee a particular request cost. Measure your own input-token profile and account for any applicable regional or long-context pricing.
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How should you configure for reliable decisions?
Probability and confidence fields are model outputs, not proof of application accuracy. A high confidence value does not by itself establish that a decision is correct for your data or use case. OpenAI recommends using labeled examples from the application to set thresholds for routing, filtering, or review, with thresholds chosen according to the consequences of false positives and false negatives.
- Define the decision criterion. State what evidence in the input makes a predicate true, what distinguishes each choice, or what qualifies for each score level.
- Cover cases outside named categories. Add a fallback choice when the listed choices may not fit every input, then evaluate how often it is used and whether those cases need review.
- Build a representative labeled set. Include ordinary cases, edge cases, and examples where mistakes have different consequences.
- Choose thresholds against error costs. Compare model outputs with the labels and decide when to accept, route, filter, or send a case for human review. The appropriate threshold depends on the application’s trade-offs, not a universal API default.
- Test operational failure paths. Evaluate timeout and transport-error handling, refusals, and uncertain results in the client application. The reviewed guide gives no endpoint-specific retry or timeout values.
When comparing configurations, use the same labeled examples and assess task quality, response-time distribution, input-token use and applicable price, and failure handling. Also establish whether your account and use case meet eligibility requirements for any data-handling or regional-processing features you intend to use.
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What privacy and regional-processing options are documented?
The guide says the Decisions API supports Zero Data Retention and HIPAA use for eligible customers, along with data residency and regional processing in the United States and Europe (EEA plus Switzerland). These are not universal defaults or guarantees of account eligibility. Review OpenAI’s data-control requirements for eligibility and limitations before designing around them.
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