Microsoft Decision 1 vs OpenAI Decisions API: A Guide

Compare Microsoft Decision 1 and OpenAI Decisions API at the boundary your application needs: what evidence goes in, what typed answer comes back and what your software does with uncertainty. A lower token price or a strong vendor benchmark does not settle that choice. The useful comparison starts with one repeatable decision, such as routing a customer request to the correct team.

Product documentation and current access details were checked on October 11, 2026. The workflow examples are proposed evaluation designs, not reports of completed customer deployments.

Microsoft’s announcement describes Decision 1 as a model available through Microsoft Foundry and OpenRouter. Microsoft says it post-trained Qwen3.5-9B for fixed-option decision scoring. Its author archive dates the announcement October 9. The broader release context should not be confused with the exact configuration of a particular endpoint.

OpenAI’s current Decisions documentation, checked October 11, describes a public beta using the dedicated POST /v1/decisions endpoint, with gpt-6-luna as the supported model. It accepts text and images and provides predicate, choice and score question types. Those current details are an October 11 check, not an assumption that beta status or the supported-model list will remain unchanged.

For procurement, write down the actual route you intend to use on each side. The commercial relationship, deployment controls and request contract belong to that route. A model being available in a catalogue does not tell you whether your account has the required access, region or quota. Resolve those operational facts before investing in a comparison that cannot be deployed under your organization’s conditions.

Consider a fictional support inbox with billing, delivery, technical and other queues. The decision is where a request should go, not how to write a reply. Define what each queue owns and include examples of ambiguous requests. If a message describes both a charge and a delivery issue, the policy should explain whether to choose a primary owner or request review.

An incomplete choice set can make an accurate model appear wrong. If the only choices are billing and technical, a delivery request still has to land somewhere unless the application provides another path. Include an appropriate unknown or review outcome when the task allows it. Do not interpret a forced choice as evidence that the system found an appropriate destination.

The decision-model overview explains the category. This procurement comparison goes one level further: can the same business decision be expressed clearly through both candidate interfaces? Keep the label definitions and evidence identical where possible. If one API requires a different representation, document that difference rather than pretending two superficially similar requests are the same test.

One useful ambiguity case is a message saying that an order arrived late and was also charged twice. If the business policy says billing owns duplicate charges while delivery remains a secondary issue, the expected primary route is explicit. If the business has no such rule, the correct outcome may be review. Do not ask the model to create that policy during the test. A disagreement between two models can expose an unresolved operating decision rather than a quality difference, and resolving it improves both candidates’ evaluation.

• Define the decision before choosing an API.
• Make option descriptions distinct and operationally meaningful.
• Provide a path for cases that do not fit the supported categories.

A probability that a condition is true, a choice among departments and an ordered severity score are not interchangeable outputs. OpenAI documents these as separate question types. Microsoft describes fixed options including yes/no, multiple-choice and rating tasks. The application must still define how an answer maps into an operational state and what happens when the answer is missing or refused.

For inbox routing, a choice is usually easier to interpret than a numeric score whose ranges secretly correspond to departments. For a review rubric, ordered levels may make sense, but their definitions need to be explicit. A number between levels should not acquire more precision than the underlying rubric supports. The interface can return a well-formed value without making the business rule well designed.

Validate the response at the application boundary. Confirm that the expected question is present, the returned type matches and the selected value is allowed. Preserve an explicit failure state rather than sending malformed results to the default queue. Our tool-error reference is relevant because the consumer of a decision API needs deliberate handling for a refused, incomplete or unavailable response just as it does for a failed tool.

A probability is useful only when the team understands how it behaves on the workload. If cases scored near 0.9 are often wrong in your domain, a threshold copied from another application will not repair the problem. Build a labelled evaluation set with examples that resemble the real requests, including ambiguous, out-of-scope and incomplete cases.

Separate two questions: does the model choose the right option, and does its confidence correspond usefully to correctness? A system can rank options well while being too confident about difficult cases. That matters when the application automatically acts above a threshold and sends the rest to review. The threshold should follow the cost of errors and the observed behavior, not a convenient round number.

Microsoft reports calibration and robustness testing in its announcement. Treat those as vendor-reported evidence that motivates your own trial, not as a guarantee for a different business population. The open decision-model comparison gives additional category context. Neither that comparison nor a supplier’s chart replaces labelled cases from the decision you actually intend to automate.

Review threshold consequences with a concrete confusion table rather than a single overall score. Count requests sent to the wrong queue, correctly handled requests unnecessarily sent to review and requests whose proper category was absent from the label set. These errors create different costs. A threshold that reduces wrong automatic routes may increase human workload, which can be the right tradeoff for a sensitive queue. The important result is that the team can see the tradeoff and choose it deliberately rather than inheriting it from a default setting.

A routing policy should not change because the allowed options were listed in a different order. Try reordering the choices, paraphrasing their descriptions and adding harmless formatting changes to the input. Keep the intended meaning unchanged. These tests can expose dependence on presentation that an ordinary accuracy score on one fixed prompt would miss.

Also test meaningfully changed inputs. Removing the order identifier or adding a second issue should sometimes change the appropriate response. A model that never changes its decision is not robust if it ignores important new information. Pair invariance tests with sensitivity tests so the evaluation checks both stability under irrelevant changes and responsiveness to relevant ones.

Version the question definitions as part of the application. A support team that expands what billing owns has changed the decision task even if the API and model stay the same. Reuse the earlier test cases to see whether the new description creates unintended routing shifts. This is a practical reason to keep examples and expected outcomes outside a vendor playground: the business policy will evolve independently of the provider.

• Reorder choices without changing their meaning.
• Paraphrase descriptions while preserving the policy.
• Add or remove a relevant fact and verify the intended response changes.

Microsoft’s announcement lists $0.042 per million input tokens and no output-token charge for Decision 1. That does not make a request free. A hypothetical 2,000-token input at that rate costs $0.000084 for model input alone. Record the endpoint and checked date, and verify the complete billing terms before using the figure in a forecast.

OpenAI’s Decisions guide checked October 11 lists $0.10 per million input tokens for gpt-6-luna on /v1/decisions, with no cache-read, cache-write or output-token charges. A hypothetical 2,000-token input at that base rate costs $0.0002. Regional processing premiums and long-context input multipliers can apply, so confirm those conditions and actual billed usage before comparing complete workflows. Include evidence preparation, image handling where used, retries, review and any supporting model calls. A tiny decision request may sit inside a much more expensive pipeline.

Measure end-to-end latency from the application’s perspective. The time to fetch evidence, encode an image, wait for a request and apply the response can matter more than a model-only benchmark. Keep median and slow-tail behavior distinct if your workflow has a response deadline. This article does not reproduce either vendor’s speed claims or declare a head-to-head latency winner.

A procurement trial should also test an unavailable service. Decide whether the application holds the request, uses a validated fallback or sends it to a person. Measure the effect on the actual work queue, including whether a retry can create a duplicate assignment. A model’s ordinary speed is useful, but the team must still handle the day when the route is slow or unreachable. Keep fallback decisions in the same outcome log so the commercial comparison includes the cost of maintaining a usable service.

A classification result should feed a defined action policy. In the fictional inbox, routing a request to a queue is usually a different consequence from refunding an order or changing an account. Do not expand authority simply because the decision model can return a confident yes. The application still needs identity, authorization and any required review before a consequential action.

Start with shadow evaluation if the workflow is already operating. Let the candidate produce decisions that are recorded but do not change the live destination. Compare them with the current process and inspect disagreements. This exposes integration and policy issues without making customers absorb the cost of the first experiment. Decide in advance how disputed labels will be reviewed.

Our AI transformation service can help connect that evaluation to a narrow rollout. The useful question is which task can safely use the result under an explicit policy. A decision model can make an application faster or cheaper, but it cannot assume responsibility for a business rule that the team has never written down.

• Keep authorization outside the model score.
• Record shadow decisions and disagreements before enabling actions.
• Expand the consequence only when the acceptance evidence supports it.

The comparison may favor an existing platform relationship, a particular input requirement or a response contract that your team can maintain easily. It may also show that both candidates are good enough for a narrow task and that operational fit matters more than a small score difference. Record those reasons explicitly so the decision survives the next vendor announcement.

Keep the evaluation portable. Store labelled inputs, question definitions, expected outcomes and scoring notes in your own process. If the supported model, beta status or serving path changes, you can repeat the relevant tests without rebuilding the business question. This also prevents a vendor dashboard from becoming the only record of why an automated action was considered acceptable.

Microsoft Decision 1 and OpenAI Decisions API are worth comparing where software needs a bounded answer. The strongest choice is the one that expresses your decision clearly, handles uncertainty predictably and fits your deployment conditions. A reliable procurement result names that task and those conditions, rather than announcing a universal winner from two different vendors’ demonstrations.

Make the final recommendation legible to an operator: this route handles these question definitions, above the chosen threshold, with these exceptions sent to review. Include the evidence date and the owner who can revise the rule. That concise operating statement is the practical product of the comparison, even when the detailed evaluation contains many more measurements.

• Confirm access and current terms for the exact route.
• Compare the same labelled task and explicit uncertainty policy.
• Write the adoption decision with its limits and retest triggers.