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Microsoft-Decision-1 is for applications that need a bounded, typed judgment instead of a paragraph of generated text. Send one text or JSON state object and a map of questions, then read the typed answer and its probabilities. Use noul for a true/false condition, choice for one label from a fixed set, and score for an ordered scale. Your application can then route work, set a severity, or decide whether another check is needed.
The practical workflow is: deploy the model in Microsoft Foundry, call the documented system endpoint with your deployment name, validate every returned answer against the question you sent, and apply an explicit confidence and abstention policy. This guide shows that workflow, a support-ticket example, multi-question requests, integration patterns, cost and performance claims reported by Microsoft, and the safety controls needed before production use.
What Microsoft-Decision-1 does
Microsoft Learn describes Microsoft-Decision-1 as a decision-scoring model rather than a generative language model: it does not produce a free-form response or written rationale. It makes a focused judgment about text or JSON and returns a typed, numerical decision. Microsoft Foundry Labs describes it as being built on Qwen3.5-9B for fast, single-pass decision scoring.
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That distinction determines where it belongs in an application. A generative model is appropriate when the product must write, summarize, rewrite, explain, or otherwise create content. Decision-1 is appropriate when the product already knows the possible outcomes and needs a consistent machine-readable result, such as a queue label, a yes/no eligibility check, or a severity score.
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The model does not remove the need to define policy. Your question instructions and criteria establish what each answer means; your application decides what to do with the answer, its probabilities, and an abstention case.
Plan the decision contract before deployment
Write down the contract between your application and the model before opening the API. A useful contract specifies:
- The exact state supplied for each evaluation, as text or JSON.
- The question name, question type, instructions, and criteria.
- The allowed labels or score range that your application will accept.
- The probability threshold for automation and the rule for abstaining.
- The fallback when the response is malformed, contradictory, or unavailable.
- The human owner for high-impact or ambiguous cases.
Keep each question narrow. One question should express one judgment. If a prompt asks for a label, a justification, and a recommendation at the same time, the output contract becomes harder to validate and the result is no longer a clean typed decision.
Deploy Microsoft-Decision-1 in Microsoft Foundry
Prerequisites
You need an Azure subscription, a Microsoft Foundry project, and permission to manage model deployments in that project. The deployment workflow documented by Microsoft is:
- Create or select the Azure subscription that will own the resource.
- Create a Microsoft Foundry project, or open an existing project where you have deployment permissions.
- Open the model catalog and select
Microsoft-Decision-1. - Create a deployment and record the deployment name exactly as entered.
- Copy the resource endpoint and create or retrieve an API key if you are not using Microsoft Entra ID.
Microsoft recommends Microsoft Entra ID for authentication. API-key authentication is also supported. Store credentials in a secret manager, not in source code, browser JavaScript, tickets, or log messages. Give the calling service only the permissions it needs and rotate keys according to your organization’s policy.
Endpoint and model name
The documented provider endpoint is:
<your-foundry-resource-endpoint>/providers/microsoft/v1/systemoneIn the request, the model value is your deployment name, not necessarily the catalog name. The response identifies the underlying model. Keep both values in your configuration so deployment changes do not silently change the route you intended to call.
Build the request
A request has three essential parts: model, state, and questions. The state can be plain text, such as a support ticket, or structured JSON containing fields your instructions refer to. The questions value is a map keyed by names that your application chooses. Each question supplies a type and instructions; criteria define the meaning of binary outcomes or the fixed labels available to a choice.
Use noul for a binary condition
Use noul when the only decision is whether a condition is true or false. State the condition so that a reviewer can tell exactly what counts as true. Criteria should define both values, including what to do when the state lacks enough evidence.
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Examples include “Does this ticket report an outage affecting multiple customers?” or “Is the requested document present?” A binary question should not hide a third outcome. If uncertainty matters operationally, include an abstention policy in your application, or choose a fixed-label question that includes a cannot tell option.
Use choice for one label
Use choice when exactly one label must be selected from a fixed set. Give every label a concise, mutually understandable description. Include an explicit cannot tell or equivalent option when the state may not support a reliable classification.
For a support queue, labels might be billing, engineering, support, and cannot tell. Define “billing” by the issue described, not by words that happen to appear in a ticket. Clear criteria reduce the chance that wording, tone, or option order determines the result.
Use score for an ordered scale
Use score when the result has an ordered meaning, such as low, medium, and high severity represented by a numeric scale. Explain what the endpoints and intermediate values mean. A score is not automatically a calibrated probability or a measurement of business loss; your team must validate how it relates to the action you intend to take.
Question-type comparison
| Type | Use it for | Returned information | Design guidance |
|---|---|---|---|
noul |
A binary condition | The typed binary answer and its probability | Define true and false criteria explicitly; handle uncertainty outside the binary contract. |
choice |
Exactly one label from a fixed set | The selected option plus probabilities | Make labels mutually clear and include an abstention option when evidence can be incomplete. |
score |
An ordered scale | A score value plus probabilities | Describe the scale and validate what each range means in your domain. |
Illustrative request payload
The following shows the shape of a support-ticket request. It is an example of the documented fields, not a substitute for the current API schema used by your SDK or deployment.
{
"model": "support-routing-prod",
"state": {
"ticket_id": "T-1842",
"subject": "Invoices fail after account upgrade",
"body": "The last two invoices show an unexpected charge."
},
"questions": {
"route": {
"type": "choice",
"instructions": "Select the single queue that should handle this ticket.",
"criteria": {
"billing": "Questions about invoices, charges, refunds, or payment records.",
"engineering": "A reproducible product defect or service failure requiring code investigation.",
"support": "How-to questions or account assistance that do not require billing or code investigation.",
"cannot tell": "The state does not contain enough evidence to select a queue."
}
},
"urgent": {
"type": "noul",
"instructions": "Is there evidence of a current outage affecting multiple customers?",
"criteria": {
"true": "The state describes a current, multi-customer service interruption.",
"false": "The state does not establish a current, multi-customer interruption."
}
},
"severity": {
"type": "score",
"instructions": "Score operational impact from 1 (minor) to 5 (critical).",
"criteria": {
"1": "Minor inconvenience with a straightforward workaround.",
"3": "Material impact requiring prompt attention.",
"5": "Critical impact with no practical workaround."
}
}
}
}Use the exact property names and value constraints required by the API version or client library you adopt. Treat the payload above as a readable design pattern: one shared state, several independent questions, and criteria that make the intended distinctions explicit.
Send the call and authenticate it
Send the request to the system endpoint for the Foundry resource and authenticate with Microsoft Entra ID or an API key. Keep the endpoint, deployment name, and authentication method in server-side configuration. Do not expose an API key to a mobile or browser client; proxy the request through a service you control when a client application needs a decision.
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- Generate a correlation ID in your application.
- Build the state and questions from validated input.
- Send the call to
<your-foundry-resource-endpoint>/providers/microsoft/v1/systemone. - Record latency, HTTP outcome, deployment name, and correlation ID. Redact ticket bodies and other sensitive state unless retention is explicitly approved.
- Parse the response and verify that an
answersobject exists. - For each question, verify the returned type, allowed value, and probability structure before taking action.
Retry only transient failures and use bounded backoff. A timeout or malformed response is an application failure, not evidence for any particular business answer. Route such cases to the fallback path you defined in your contract.
Validate and use the response
Check structure before meaning
The official support-classifier sample checks that answers exists, that the answer has type choice, and that the returned label belongs to the allowed options. Follow the same order in production:
- Confirm the response is valid JSON and contains
answers. - Confirm every question you requested has an answer with the expected type.
- Confirm a
choicelabel is one of your configured labels. - Confirm a
scorevalue is inside the scale you defined. - Confirm probabilities are present, numeric, and associated with the returned alternatives.
- Reject or quarantine any answer that fails validation; do not coerce an unknown label into a known one.
Use a versioned question configuration so the validator and the prompt criteria change together. Store the question version with the routing event, allowing you to explain which contract produced an action.
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A probability is useful only when it is connected to a decision rule. Choose thresholds using representative labeled data and the cost of each error, not a generic percentage borrowed from another task.
| Observed situation | Suggested application action | Why |
|---|---|---|
| High probability and low cost of an incorrect action | Automate the route or next step, while logging the event for monitoring. | Fast automation is reasonable when errors are inexpensive and reversible. |
| Intermediate probability or competing options are close | Request another evaluation, ask for more information, or place the item in a review queue. | Ambiguity should not be hidden by forcing a label. |
Low probability, an explicit cannot tell, or a high-cost action |
Abstain and escalate to a trained person. | The cost of an incorrect decision exceeds the benefit of immediate automation. |
For noul, read the probability associated with the binary judgment. For choice, inspect both the selected label and the distribution across alternatives. For score, use the score and its probabilities together; do not treat the numeric score alone as certainty.
Combine decisions in one request
Questions that use the same state can be combined. In the support example, routing, outage detection, and severity are evaluated against one ticket snapshot, which keeps the inputs consistent and avoids three separately assembled prompts. Name each question for its business purpose and validate each answer independently.
Combining questions does not mean that one answer explains another. A ticket can be routed to billing, have a low outage probability, and still receive a high severity score because a financial impact is substantial. Keep downstream rules explicit rather than inferring relationships the model was not asked to establish.
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Separate requests may be preferable when questions require different state views, different access controls, or different review owners. Do not place sensitive fields into a shared state merely for convenience.
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Support-ticket classifier: an end-to-end pattern
1. Define labels and ownership
Start with the queues your organization actually staffs. For each label, write a short criterion and an owner. Add cannot tell if tickets often omit the evidence needed to distinguish queues. Avoid labels that overlap so heavily that two reviewers would disagree about their meaning.
2. Normalize the state
Pass the subject, body, and only the metadata needed for the questions. Normalize encoding and remove duplicated signatures or automated footers that can distract from the issue. If the ticket contains secrets or regulated personal information, redact it before sending it to the model according to your data-handling policy.
3. Evaluate and gate automation
Call Decision-1, validate the answer, and compare its probability with a threshold selected from a labeled validation set. Route only high-confidence cases that have a reversible or low-impact consequence. Send ambiguous cases to a queue that a person can review.
4. Preserve an audit record
For each automated route, retain the question version, selected label, probabilities, threshold decision, model deployment name, timestamp, and correlation ID. Retain the original ticket only when your retention policy permits it. This record lets you investigate drift without treating an unvalidated model output as a fact.
5. Measure outcomes
Sample automated decisions and compare them with reviewed labels. Track false positives, false negatives, abstention rate, latency, and error rates by relevant language, channel, or customer segment. A high average accuracy can conceal a serious disparity in a smaller group.
Decision-1 versus a generative LLM
| Question | Microsoft-Decision-1 | Generative LLM |
|---|---|---|
| Output | Typed, numerical decision with probabilities. | Free-form text or generated structured content. |
| Best fit | Routing, gating, binary checks, fixed labels, and ordered scores. | Creation, summarization, rewriting, explanation, and conversational responses. |
| Abstention | Can be designed into the option set, such as cannot tell, and tied to thresholds. |
Requires an instruction and separate validation policy; prose confidence is not a probability contract. |
| Latency and cost | Microsoft positions it for fast, single-pass scoring; reported figures are listed below. | Depends on the selected model, prompt, output length, and service configuration. |
| Evaluation focus | Calibration, label accuracy, option-order and wording sensitivity, and disparity patterns. | Content quality, instruction following, factuality, and output safety in addition to task accuracy. |
| Need for explanations | It does not generate a written rationale. | Can generate explanations, though those explanations still require verification. |
These approaches can be combined. Use Decision-1 to gate or route a request, then invoke a generative model only for the response that a person will read. The typed decision remains the control signal; the generated text remains a separate output that needs its own safety and quality checks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Performance and pricing claims reported by Microsoft
Microsoft reported results from 36 benchmarks covering nearly 150,000 questions in 2026. In those measurements, Microsoft says Decision-1 was 2.5 times faster than the measured runner-up and 35 times faster than GPT-6 Sol. Microsoft also reported a price of $0.042 USD per million input tokens, with output tokens free. These are Microsoft-reported benchmark and pricing claims, not independent testing; actual latency and spend depend on request size, concurrency, region, quotas, retries, and your deployment configuration.
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Evaluate calibration before automation
Build a labeled evaluation set that reflects the language, channels, edge cases, and class frequencies your application will see. Split development data from a final holdout set. For each question type, measure more than top-line accuracy:
- For binary questions, inspect false-positive and false-negative rates at the threshold you plan to use.
- For fixed choices, measure per-label precision and recall, confusion between neighboring labels, and the rate of
cannot tell. - For scores, test whether higher values correspond to the operational outcomes the scale is supposed to represent.
- For probabilities, compare predicted confidence with observed correctness; a high-confidence error deserves immediate investigation.
- Repeat tests after changing wording, label descriptions, or option order.
Keep instructions and criteria concise, neutral, and stable during an evaluation run. If you randomize option order as a robustness check, record the order used for each request and look for systematic changes. A model that changes its answer when labels are merely reordered needs tighter criteria, a safer threshold, or human review.
Limitations, fairness, and responsible use
Microsoft identifies 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, not footnotes.
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- Write criteria in neutral language and avoid proxies for protected characteristics.
- Consider randomizing option order during testing to detect positional effects.
- Monitor error and disparity patterns after deployment, not only during launch approval.
- Provide a human-review path and a way to correct an automated outcome.
- Disclose AI involvement when your policy or law requires it, and explain how a person can request review.
For credit, employment, housing, healthcare, legal matters, or other consequential decisions about people, use meaningful human review. Do not make Microsoft-Decision-1 the sole decision-maker for an individual’s access, eligibility, safety, or rights. A probability threshold is not a substitute for legal compliance, domain expertise, or procedural safeguards.
Troubleshoot common integration failures
The service rejects the request
Check that the URL uses the documented /providers/microsoft/v1/systemone path, that the deployment name in model matches the deployed name exactly, and that the caller has permission to invoke the deployment. Confirm that the request contains a state and a questions map, and that every question has a supported type and complete criteria.
The answer is missing or has the wrong type
Stop downstream automation when answers is absent or a returned answer does not match the requested type. Log the correlation ID and a redacted response shape, then send the item to your fallback path. Do not silently reinterpret a malformed answer.
The label is not one of your options
Reject it as invalid. Check for configuration drift between the criteria sent to the model and the labels accepted by your validator. Deploy the question definition and validator as one versioned unit.
Confidence is high but outcomes are poor
Recheck the label definitions, class balance, wording sensitivity, and threshold selection against fresh reviewed data. Inspect errors by segment and by option order. A high probability does not prove calibration on your workload.
Latency or cost is higher than expected
Measure state size, request concurrency, retries, and queueing in your own region. Remove irrelevant state fields, avoid retry storms, and compare observed usage with the current Foundry billing and quota information for your deployment.
Quick Recap
Production checklist
- Azure subscription and Foundry project are owned by the correct team.
- Deployment permissions, authentication, secret storage, and key rotation are configured.
- Endpoint and deployment name are stored as environment-specific configuration.
- Each question has one purpose, a supported type, clear criteria, and an allowed-value validator.
- An explicit
cannot tellor equivalent abstention path exists where uncertainty is possible. - Probability thresholds were selected from representative labeled data and tied to error costs.
- Malformed responses, timeouts, and service failures have a non-automated fallback.
- Question versions, decisions, probabilities, and correlation IDs are auditable without oversharing sensitive state.
- Monitoring covers accuracy, calibration, latency, abstention, drift, and disparity patterns.
- Consequential decisions include meaningful human review and a correction process.
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