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What Microsoft-Decision-1 is
Microsoft describes decision models as models built to produce structured outputs that software can act on immediately. Microsoft-Decision-1 is available through Microsoft Foundry and OpenRouter. Its intended jobs include routing, classification, prioritization, verification, and workflow control rather than composing a long answer.
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The model was post-trained from Qwen3.5-9B. Its structured API supports several fixed-output patterns:
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- Multiple choice: select one route, category, action, or candidate from a defined set.
- Ratings: assign a score to a response, item, or agent action.
- Rubric-based grading: evaluate an AI answer or action against criteria you specify.
Instead of asking the model to write “the best next step,” your application supplies the possible next steps and receives a machine-readable result. That output can trigger a queue, block an action, choose a specialist, select a search result, or hand the case to another model.
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Microsoft’s Achint Srivastava summarized the distinction this way: “Unlike LLMs, which are designed to generate text or reason through complex problems, decision models are purpose-built to deliver structured outputs that software can immediately act on.”
Decision-1 and chat solve different problem shapes
| Decision point | Microsoft-Decision-1 | Chat-based interface |
|---|---|---|
| Output contract | A selected option, rating, class, or probability distribution that code can parse. | Generated language, usually accompanied by application-specific parsing or tool calls. |
| Best task shape | Fixed labels, thresholds, rankings, gates, routes, and verification. | Open-ended dialogue, explanation, synthesis, clarification, and preference discovery. |
| User experience | Usually invisible orchestration behind a screen, workflow, or agent. | The visible conversational experience in which the user reads and responds. |
| What the product team must define | The answer options, criteria, allowed output, and confidence policy. | The prompt, conversation state, response handling, and any tools or guardrails. |
| Latency and price reported by Microsoft | Very low latency and input-only pricing; Microsoft reports $0.042 per million input tokens, with output tokens free. | Depends on the selected chat model and its input/output pricing; no comparable figure is established here. |
| Robustness claim | Microsoft reports stable choices when options are paraphrased, reordered, or shuffled. | Behavior depends on the prompt, conversation history, and generation settings. |
The table describes an architectural distinction, not a claim that one model is universally more intelligent. A chat model can discuss a decision in detail; Decision-1 can make a bounded decision consistently and hand it to software.
When Decision-1 is the better choice
Routing and escalation
Use Decision-1 when every request must be assigned to one of a known set of destinations. A game-support service might route an account problem, purchase problem, technical fault, or safety report to different queues. An agent might route a player’s request to the correct tool or skill. The application can reject an invalid label instead of trying to interpret a paragraph of generated text.
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Classification and labeling
For moderation, intent analysis, content classification, data labeling, or code scanning, the useful result is a category and confidence, not an essay. A predefined taxonomy also makes reporting and quality review possible: you can count each class, inspect low-confidence cases, and change the taxonomy without redesigning the user interface.
Eligibility gates and verification
A yes/no or rubric decision is appropriate when a workflow has a gate. Examples include checking whether an AI agent’s proposed action satisfies a policy, whether submitted data passes validation, or whether a recommendation meets a minimum set of constraints. Keep the authoritative business rules in ordinary code where possible; use the model for the language-dependent judgment and then apply a deterministic gate to the result.
Prioritization, ranking, and next-action selection
Decision-1 can select among candidate actions, rank items, or choose a next step. In a game operations dashboard, it could prioritize incident reports or select which player-feedback theme should be reviewed first. In a search or recommendation pipeline, it can score candidates against a rubric before another component renders the result.
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High-volume, latency-sensitive controls
Microsoft reports approximately 35 times faster median latency than GPT-6 Sol in its 2026 comparison. That is a Microsoft-reported benchmark, not an independent measurement, and your network, prompt size, concurrency, and service region will affect actual latency. The architectural implication is still important: a small, fixed-output decision call can sit on a critical path where a long generative response would be wasteful.
When a chat interface works better
Exploring an unclear request
If the user has not supplied enough information to choose among your options, chat can ask a targeted question and incorporate the answer. A player who says “I want a challenging build” may need to clarify platform, game mode, preferred play style, available equipment, or tolerance for complexity before any fixed recommendation is meaningful.
Explaining trade-offs
Users often accept a recommendation more readily when they can see why it fits and what they give up. Chat can explain that one option is cheaper but slower, safer but less flexible, or easier to learn but less powerful. Decision-1 can supply the selected option and probabilities; a chat model can turn that structured result into a readable explanation without being responsible for the underlying classification.
Follow-up and revision
Conversation is the right interface when a user wants to revise a request: “Keep the same constraints, but make it suitable for co-op,” or “What changes if I only have 20 minutes?” The chat layer maintains context, asks the next question, and can call Decision-1 again after the state changes.
Synthesis and open-ended generation
Summarizing feedback, drafting a plan, comparing several prose documents, or producing instructions requires language generation. A fixed label cannot carry all of the nuance a reader needs. Decision-1 can still judge, filter, or route the generated material, but it is not a replacement for the explanatory response.
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It depends on what “recommendation” means in your product. If the system must choose one item from a known catalog under explicit constraints, Decision-1 is a strong selector. If the user is still discovering preferences or wants a reasoned conversation, chat is the better front end.
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Separate the recommendation into two jobs:
- Candidate decision: define the allowed options and criteria, then ask Decision-1 to select or rank them.
- Human explanation: pass the selected option, relevant factors, and any uncertainty to a chat model that can explain the result and invite correction.
This division prevents a fluent explanation from silently changing the recommendation. It also lets the product log the actual decision independently of the prose shown to the user.
The hybrid pattern: decision first, conversation second
A practical hybrid flow keeps the structured decision authoritative while allowing a natural user experience.
- Collect state and constraints. Capture the user’s request, known preferences, eligibility data, and hard limits in your application state. Do not force the model to infer facts that your software already knows.
- Define the candidate set. Give Decision-1 the currently valid labels or actions, with concise descriptions and the rubric used to distinguish them.
- Classify, check, rank, or select. Ask for the structured result. Store the chosen option, probabilities, model version, input state, and timestamp.
- Apply confidence thresholds. If the top probability is below your accepted threshold, or if the top two options are too close, route the case to review or to a chat model for clarification. The threshold is a product policy you must establish and validate; Microsoft does not prescribe one in the cited material.
- Explain the result. Give the chat model the structured decision and the evidence it may discuss. Instruct it not to replace the selected option unless a new decision call is made.
- Continue the conversation. When the user adds or changes a constraint, update the state and call Decision-1 again rather than relying on the previous answer.
- Log separately. Keep the machine decision and the generated explanation as separate records so reviewers can tell whether an error came from classification, policy, or wording.
A minimal internal record can look like this:
{
"options": ["route_a", "route_b", "human_review"],
"selected": "route_a",
"probabilities": {"route_a": 0.81, "route_b": 0.12, "human_review": 0.07},
"threshold_policy": "auto_route_if_top_probability_at_least_0.75",
"explanation_status": "generated_after_decision"
}The field names are illustrative. Your implementation should use the schema exposed by the service you deploy and preserve the raw response needed for audit.
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What Microsoft’s published figures show
| Measure | Reported result | How to interpret it |
|---|---|---|
| Benchmark coverage | Nearly 150,000 questions across 36 benchmarks. | Microsoft says Decision-1 achieved the highest accuracy in that comparison. The published comparison does not provide independent replication or the full benchmark breakdown. |
| Median latency | Approximately 35 times faster than GPT-6 Sol. | A Microsoft 2026 comparison. Production latency will vary with deployment conditions. |
| Decision stability | 1.3% average decision flips across eight perturbations. | Microsoft reports zero flips when option descriptions were paraphrased or when options were reversed or shuffled. The figure describes its test set and perturbations, not every possible prompt. |
| Xbox feedback processing | More than 10,000 feedback and review items. | Microsoft says Decision-1 produced researcher-defined themes with quality competitive with GPT-6 Sol, more than 14 times faster, and 200 times less expensive. |
| Input pricing | $0.042 per million input tokens; output tokens free. | Microsoft’s published price for Decision-1. Confirm the current rate, service terms, and any platform charges before budgeting. |
These are Microsoft-reported benchmarks or internal tests. They are useful for understanding the intended position of the model, but they do not guarantee a particular accuracy, latency, or savings level for your taxonomy, language, traffic pattern, or deployment.
How to design a reliable Decision-1 integration
Make options mutually understandable
Each label should represent a real action or category, not an overlapping synonym. “Technical issue,” “performance issue,” and “bug report” need boundaries that a reviewer can apply consistently. Include a human-review option when no automatic category safely covers the case.
Put hard rules outside the model
Use ordinary validation for permissions, account ownership, age restrictions, payment state, and other deterministic facts. Decision-1 can interpret unstructured text or assess a rubric, but it should not be the sole enforcement mechanism for rules your software can check exactly.
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Measure calibration, not just top-label accuracy
A probability is valuable only if it helps you choose an action. Build a labeled validation set representative of real traffic. Measure false positives and false negatives for every important class, inspect calibration by confidence band, and set separate thresholds where the cost of an error differs. A safety-screening decision may require human review at a much lower confidence than a low-impact content tag.
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Because Microsoft specifically reports robustness to paraphrased descriptions and reordered options, include those perturbations in your own regression suite. Also test missing fields, contradictory constraints, slang, long inputs, multilingual text if relevant, and adversarial attempts to force a label. Record the exact option descriptions used in each test.
Version the decision contract
Store the model identifier, option list, rubric, threshold policy, and prompt or request template with each decision. Changing a label description can change historical behavior even when the model has not changed. A versioned contract makes rollbacks and audits possible.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and fixes
The option set does not contain the right answer
Symptom: the model chooses a plausible but inappropriate label because every available choice is wrong. Fix: add an explicit “unknown,” “not enough information,” or human-review route, and monitor how often it is used. Do not interpret a high probability as proof that the option set was complete.
Low confidence is shown as certainty
Symptom: the interface presents a borderline classification as an unquestionable recommendation. Fix: apply thresholds in code, expose uncertainty to the workflow, and send ambiguous cases to clarification or review. Keep the user-facing explanation consistent with the actual confidence.
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The chat explanation changes the decision
Symptom: the prose recommends a different option from the structured record. Fix: pass the selected option as locked context, instruct the chat layer to explain rather than reselect, and trigger a fresh Decision-1 call whenever new information changes the request.
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Long conversations degrade the classification
Symptom: an old preference or irrelevant message affects a later route. Fix: maintain a compact, application-owned state object and send only the facts and current request needed for the decision. Let chat retain conversational history for the user experience, not as the sole source of truth for routing.
Latency gains disappear in the surrounding system
Symptom: the decision call is fast, but retrieval, network hops, logging, or a subsequent chat response dominates the user-visible time. Fix: measure each stage separately, cache stable candidate data, run independent checks concurrently where safe, and make the chat explanation asynchronous when the workflow permits.
Examples for game and interactive-service teams
| Product task | Decision-1 role | Chat role |
|---|---|---|
| Player-support intake | Classify the request as account, purchase, technical, gameplay, or safety and route it. | Ask for missing details and explain what happens next. |
| Build or loadout selector | Choose or rank valid builds from constraints such as mode, equipment, and play style. | Explain strengths, weaknesses, and alternatives in plain language. |
| Community moderation | Assign a policy category, urgency, and review route. | Draft a respectful notice or answer an appeal after policy review. |
| Agent tool control | Select the permitted skill or next action and gate risky actions. | Tell the user what the agent is doing and answer follow-up questions. |
| Feedback analysis | Group reports into researcher-defined themes and prioritize them. | Summarize the themes for a team and surface representative examples. |
| Search relevance | Score or select candidates against a fixed relevance rubric. | Explain why a result fits and refine the search conversationally. |
These are application patterns, not claims that Microsoft has benchmarked each gaming scenario. Validate the labels and thresholds against your own data.
Cost, speed, and operational trade-offs
Decision-1’s reported $0.042 per million input tokens and free output tokens make it attractive for high-volume classification, provided the published rate applies to your access route and account. A million input tokens is a usage quantity, not a flat subscription; your total bill can also depend on the hosting channel, storage, retrieval, monitoring, and any chat model used for explanations. Confirm current terms in Microsoft Foundry or the provider through which you call it.
Do not choose solely on token price. A chat explanation after every decision may cost more and take longer than the decision call itself. Conversely, using a decision model for a task that requires several clarification turns can create a poor experience and repeated calls. Estimate the complete workflow: number of decision calls, average input size, clarification rate, human-review rate, and percentage of cases that receive a generated explanation.
A practical selection checklist
- Choose Decision-1 when the application can enumerate the valid outcomes and needs a score, route, gate, rank, or label.
- Choose chat when the user must explore preferences, ask follow-ups, understand trade-offs, or generate a flexible response.
- Choose a hybrid when the product needs both a dependable machine action and a natural explanation.
- Define an explicit fallback for uncertainty, missing information, and out-of-scope requests.
- Keep deterministic business rules in code and log the decision separately from generated prose.
- Benchmark your own representative cases; treat Microsoft’s 2026 figures as reported evidence, not a guarantee for your workload.
Bottom line for guided choices
Microsoft-Decision-1 is the better fit when “choose” means selecting from a controlled answer space and immediately triggering software behavior. Chat is the better fit when “choose” means helping a person discover what they want, understand alternatives, or revise a request through conversation. For most polished products, use Decision-1 as the bounded decision and routing layer, then use chat to clarify and explain without silently changing that decision.
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