Microsoft-Decision-1 is designed for one job: turning text or JSON into a typed decision instead of a paragraph of generated prose. You deploy it through Microsoft Foundry, send a shared state object plus one or more typed questions, then validate the returned answer and use its probabilities to route, gate, abstain, or escalate. Use noul for a binary condition, choice for one label from a fixed set, and score for an ordered scale.
The safest production pattern is to treat the result as a decision signal, not an unquestionable verdict: define neutral criteria, validate every response, set confidence policies from representative labeled data, and keep a person in the loop for high-impact decisions.
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What Microsoft-Decision-1 does
Microsoft Learn describes Microsoft-Decision-1 as a focused decision model rather than a generative language model. It does not produce a free-form answer or written rationale. It makes a typed, numerical judgment about supplied text or JSON and returns that judgment in a structured response.
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“Unlike a generative large language model (LLM),
Microsoft-Decision-1doesn’t generate a free-form response or written rationale. It makes a focused judgment about text or JSON and returns a typed, numerical decision.”#1 Best Overall
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Microsoft Foundry Labs describes the model as being built on Qwen3.5-9B for fast, single-pass decision scoring. That makes it useful when the next application action is known in advance: route a support ticket, check whether a condition is present, assign a severity level, or decide whether another workflow should run.
The model is not a replacement for content generation. If an application must draft an email, summarize a case, rewrite text, or explain a result, use a generative model for that part. A common architecture uses Decision-1 first as a router or gate and invokes a generative model only after the route is known.
What you need before calling the model
An Azure and Foundry environment
The documented deployment workflow requires an Azure subscription and a Microsoft Foundry project. Your identity also needs permission to manage model deployments in that project.
A deployment and its connection details
Deploy Microsoft-Decision-1 from the Foundry model catalog. After deployment, record three values:
- The Foundry resource endpoint.
- The deployment name you selected.
- An API key, unless your application uses Microsoft Entra ID authentication.
The provider endpoint documented for this model has this form:
<your-foundry-resource-endpoint>/providers/microsoft/v1/systemone
Microsoft recommends Microsoft Entra ID for authentication. API-key authentication is also supported, which can be useful for a controlled server-side integration, but the key must remain in a secret store and must never be shipped to a browser or mobile client.
A decision contract
Before writing application code, define what each question means, what an acceptable answer looks like, and what happens when confidence is insufficient. Write criteria that a different engineer could apply without guessing. Include an explicit abstention label such as cannot tell when a wrong automatic decision would be costly.
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A request contains a deployment model name, a state value, and a questions map. The state can be plain text or JSON. Each question specifies a type and instructions; criteria can define the meaning of binary outcomes or describe the fixed choices available to the model. Answers are returned under the same question names, so stable names make downstream parsing straightforward.
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The following is an illustrative request body. Keep the field names and values aligned with the current schema exposed by your Foundry deployment:
{"model":"support-decision-prod","state":{"ticket_id":"T-1842","subject":"Invoice shows two charges","body":"I was billed twice for the same monthly plan. Please reverse one charge."},"questions":{"route":{"type":"choice","instructions":"Choose exactly one routing label for this ticket.","criteria":{"billing":"Payment, invoice, refund, or charge issue.","engineering":"A product defect or technical failure requiring engineering work.","support":"Account or usage help that is neither billing nor an engineering defect.","cannot tell":"The text does not provide enough evidence for a reliable route."}},"urgent":{"type":"noul","instructions":"Is there evidence that the ticket requires urgent handling?","criteria":"True only when the text indicates an outage, safety risk, deadline-sensitive loss, or similarly urgent impact."},"severity":{"type":"score","instructions":"Assign an ordered severity from 1 (lowest) to 5 (highest) using the ticket evidence."}}}In this example, all three questions inspect the same state. The model can therefore make routing, urgency, and severity judgments from one consistent snapshot rather than from separately prepared prompts that may drift apart.
Choosing the right question type
noul: a binary condition
Use noul when the question has two outcomes, such as whether a message contains a cancellation request or whether a ticket indicates an outage. The response includes the decision and its probability. Your application should define what true and false mean in the criteria instead of relying on vague words such as “important” or “bad.”
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choice: one label from a fixed set
Use choice when exactly one category must be selected. Describe every option in concise, mutually understandable criteria. Include an abstention option when forcing a label could create a harmful or expensive error.
The response provides the selected option together with probabilities. Treat the label as untrusted input until your code confirms that the answer has the expected type and that the option is in your allow-list.
score: an ordered scale
Use score for an ordinal judgment such as severity, urgency, or policy risk. State the order explicitly and define the meaning of each end of the scale; add intermediate descriptions when different values trigger different actions.
A score is not automatically a calibrated measurement. A value of 4 on a five-point scale is useful for ordering cases, but it does not by itself mean “80 percent severe.” Use the returned probabilities and validation data to decide when a score is actionable.
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Deploying and authenticating the API call
Deployment sequence
- Create or select an Azure subscription.
- Create a Microsoft Foundry project.
- Verify that your identity can manage model deployments.
- Open the Foundry model catalog and deploy Microsoft-Decision-1.
- Copy the resource endpoint, deployment name, and API key if you are using key authentication.
- Store those values in server-side configuration or a secret manager, not in source code or client-side JavaScript.
Model name versus underlying model
Set the request’s model value to the deployment name you created. The response identifies the underlying model separately. Do not substitute the catalog name for a deployment name if your project uses a custom deployment label.
Authentication choices
- Microsoft Entra ID: Microsoft recommends this approach for managed identity, role-based access, and centralized credential rotation.
- API key: Supported for applications that can protect a secret on a trusted server. Rotate it and restrict access to the smallest practical set of services.
Send the structured request to the documented provider endpoint, using the authentication and current request method shown in your Foundry project. Keep network calls on a backend so users cannot alter the decision criteria or extract credentials.
Worked pattern: classifying a support ticket
1. Define the labels
Suppose a support operation has three queues: billing, engineering, and support. Write a short criterion for each queue and add cannot tell for tickets that lack enough evidence. Avoid overlapping definitions; “technical issue” should not simultaneously mean an engineering defect and a general how-to question.
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2. Send one state with several questions
Use the ticket text and relevant structured fields as the shared state. In the example request above, route, urgent, and severity are evaluated together. This lets the application make a consistent routing decision while retaining separate signals for escalation and prioritization.
3. Validate before routing
The official sample pattern checks that an answers object exists, verifies that the route answer has type choice, and confirms that the returned label is one of the permitted options. Implement the same defensive checks before putting a ticket on a queue.
const allowedRoutes = new Set(['billing', 'engineering', 'support', 'cannot tell']);
function readRoute(result) {
if (!result || !result.answers) {
throw new Error('Decision response has no answers object');
}
const answer = result.answers.route;
if (!answer || answer.type !== 'choice') {
throw new Error('Route answer is missing or has the wrong type');
}
if (!allowedRoutes.has(answer.value)) {
throw new Error('Route label is outside the application allow-list');
}
return {
label: answer.value,
probabilities: answer.probabilities || {}
};
}The exact response property carrying the selected option follows the deployed API schema; the important controls are the same: check presence, check type, check membership, and handle malformed output as an operational error rather than silently routing the ticket.
4. Apply business rules after validation
Routing should be a separate step from model parsing. For example, a valid billing label can still be held for review when the urgency signal is high, while cannot tell can enter a human triage queue. Keeping these policies in application code makes them auditable and changeable without rewriting the decision instructions.
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Questions that share one state can be included in the same questions map. This is useful when a workflow needs multiple related signals, such as:
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- A category for routing.
- A binary check for urgent handling.
- An ordered severity score.
- A check for repeat contact or an unresolved previous case.
Combining them avoids preparing slightly different versions of the same ticket for separate calls. It also gives the application one response to log and validate. Keep each question narrow, give it a unique stable name, and avoid placing unrelated tasks in a single request merely to reduce call count. If one question needs a different state or a different evidence window, use a separate request so the decision contract remains clear.
Handle partial or malformed responses conservatively. A missing answer should not be interpreted as false, low severity, or a default queue. Mark the decision incomplete and follow the fallback path you defined for that question.
Using returned probabilities safely
Probabilities are decision-support signals, not guarantees. For noul, use the probability of the binary outcome. For choice, inspect the selected option and the probabilities for the available options. For score, use the value and its probability information to determine whether the ordered result is sufficiently supported.
Build thresholds from your own data
- Collect representative, labeled examples from the traffic your application will process.
- Run the proposed questions and record answers, probabilities, errors, and abstentions.
- Choose an automation threshold that reflects the cost of a wrong decision, not a convenient round number.
- Use a lower-confidence band for a second evaluation or abstention.
- Escalate high-cost or high-impact cases to a person even when confidence is high.
For example, an automated refund route might require a high probability and a matching account check, while a low-risk FAQ route could tolerate a lower threshold. The threshold belongs to your validated operating policy; it is not supplied by the model name or by a probability field alone.
Make abstention a normal outcome
Microsoft explicitly recommends an abstention option such as “cannot tell.” An abstention path is preferable to forcing a category when the text is incomplete, contradictory, or outside the criteria. Track abstentions separately from errors: a healthy system may abstain more often after you improve its safety boundaries.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decision-1 versus a generative LLM
| Question | Microsoft-Decision-1 | Generative LLM |
|---|---|---|
| Output shape | Typed, numerical decision with probabilities | Free-form generated content |
| Best fit | Routing, gating, binary checks, fixed labels, and ordered scores | Creating, summarizing, rewriting, or explaining content |
| Abstention design | Can include an explicit option such as cannot tell |
Requires an instruction and application policy to avoid forced answers |
| Workflow pattern | Make a focused judgment in one pass | Generate or transform text, often with a prompt and response parser |
| Evaluation priorities | Calibration, wording sensitivity, option-order effects, and error costs | Content quality, instruction following, factuality, and format compliance |
Choose Decision-1 when your downstream code needs a bounded answer and can act on a known schema. Choose a generative model when the user needs language. Combining them is often effective: Decision-1 selects a route or determines whether a workflow is allowed, and a generative model writes the subsequent response within that route.
Performance and pricing claims to interpret correctly
Microsoft reported the following figures in 2026. They are Microsoft-reported benchmark and pricing claims, not independent testing:
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Metric | Microsoft-reported figure | Qualification |
|---|---|---|
| Benchmark coverage | 36 benchmarks covering nearly 150,000 questions | Microsoft report, 2026 |
| Speed versus measured runner-up | 2.5 times faster | Microsoft benchmark, 2026 |
| Speed versus GPT-6 Sol | 35 times faster | Microsoft benchmark, 2026 |
| Input price | $0.042 USD per million input tokens | Microsoft pricing claim, 2026 |
| Output price | Output tokens free | Microsoft pricing claim, 2026 |
| Announcement date | October 9, 2026 | Microsoft Command Line announcement |
Real latency and cost will depend on your region, deployment configuration, request size, concurrency, retries, and surrounding application work. Measure those factors in your own environment before committing to an automation threshold or capacity plan.
Reliability, fairness, and wording controls
Microsoft lists fairness concerns, sensitivity to wording, reliability and calibration limits, and the possibility of missing subtle harmful content or flagging benign content. Treat these as engineering requirements rather than footnotes.
Use representative validation data
Test on examples that reflect real language, languages, formats, edge cases, and class frequencies. A balanced demonstration set can hide failures that occur in production when one category is rare or when users describe the same issue in different ways.
Keep instructions and criteria neutral
Define observable evidence and avoid emotionally loaded descriptions. Test paraphrases, spelling mistakes, reordered fields, and irrelevant details. Wording changes can alter a bounded decision just as they can alter a generated answer.
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Check option-order effects
For fixed-choice questions, consider randomizing option order during evaluation and, where the API contract permits it, during production. Compare error rates by position so the first or last label does not receive an accidental advantage.
Monitor more than accuracy
- Calibration of probabilities against observed outcomes.
- Abstention and fallback rates.
- False positives and false negatives by category.
- Error and disparity patterns across relevant user or language groups.
- Changes after modifying criteria, state fields, or deployment configuration.
Log the state version, question definitions, selected answer, probabilities, fallback action, and human correction where policy allows. This creates an audit trail without treating the model’s output as a written explanation.
When human review is mandatory
For credit, employment, housing, healthcare, legal matters, or other consequential decisions about people, do not make Microsoft-Decision-1 the sole decision-maker. Use meaningful human review, provide a way to challenge or correct the result, and document how reviewers use the signal.
Even outside regulated areas, escalate when an error can cause safety harm, irreversible account loss, significant financial impact, or a rights-affecting outcome. A high probability does not remove the need for review when the cost of being wrong is high.
Troubleshooting common integration failures
The service rejects the request
- Confirm that
modelis the deployment name, not merely the catalog model name. - Check that
stateis valid text or JSON and that thequestionsmap is present. - Verify that each question has a supported type and instructions appropriate to that type.
- Compare your body with the current schema shown by the Foundry deployment rather than assuming an older example still applies.
The answer cannot be routed
- Check that the response contains
answersand the expected question name. - Verify the answer type before reading its value.
- Reject labels outside your allow-list and send them to a safe fallback.
- Do not convert a missing or malformed answer into a default category.
Confidence is unstable
- Review ambiguous or overlapping criteria.
- Test wording variants and option-order changes.
- Inspect whether the state contains distracting fields or omits evidence needed by the question.
- Recalibrate thresholds with recent, representative labeled data.
Authentication fails
- Confirm that the endpoint belongs to the Foundry resource containing the deployment.
- Check Entra roles or API-key validity and rotation status.
- Keep the credential on the server and verify that the request is reaching the documented provider endpoint.
Production checklist
- Azure subscription and Foundry project are available.
- Deployment-management permission is granted to the deployment operator.
- Microsoft-Decision-1 is deployed and its deployment name is recorded.
- Endpoint and authentication are stored as protected configuration.
- Every question has a precise type, instruction, and criteria.
- Choice questions have mutually understandable labels and, when needed, a
cannot telloption. - Responses are checked for presence, type, allowed values, and probability fields before use.
- Thresholds are based on representative labeled data and documented error costs.
- Fallback, abstention, retry, and human-escalation paths are implemented.
- Logs capture model and question versions without exposing sensitive state unnecessarily.
- Fairness, calibration, wording sensitivity, and option-order effects are monitored after release.
- High-impact decisions include meaningful human review.
Bottom line
Microsoft-Decision-1 is most useful when an application needs a narrow, typed decision rather than an explanation. Deploy it in Foundry, send one clearly defined state with noul, choice, or score questions, validate the returned structure, and use probabilities to choose automation, abstention, or review. Its reported speed and low input price can make high-volume routing practical, but trustworthy behavior still depends on neutral criteria, representative evaluation, monitoring, and human oversight where mistakes matter.
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