Use Microsoft-Decision-1 when your application must choose from a known set of answers, and use a chat model when people need explanation, clarification, or open-ended discussion. Decision-1 produces structured choices and calibrated probabilities that software can route, gate, rank, or log. Chat interfaces produce natural language and are better at exploring ambiguous needs. For most products, the strongest design is hybrid: let Decision-1 make or narrow the operational choice, then let chat explain it and continue the conversation.
What Microsoft-Decision-1 is
Microsoft-Decision-1 is a decision model available through Microsoft Foundry and OpenRouter. It is designed for workflows in which an application defines the possible answers before the model runs. Instead of composing a paragraph, it returns a selected option and calibrated probability scores that code can use immediately.
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Microsoft describes decision models as “purpose-built to deliver structured outputs that software can immediately act on.” The model was post-trained from Qwen3.5-9B. Its structured API supports several common contracts:
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- Multiple choice: selecting one route, intent, category, or next action from a fixed list.
- Ratings: assigning a score such as 1–5 to quality, risk, or relevance.
- Rubric grading: evaluating an AI response or agent action against explicit criteria.
The important boundary is not that Decision-1 “understands” less than a chat model. It is that the product team supplies an answer space and asks for a controlled decision rather than unrestricted text.
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How a chat-based interface differs
A chat interface accepts a user’s request and generates language in response. The model can ask a follow-up question, explain a trade-off, summarize documents, propose alternatives, or revise its answer when the user adds context. Its output is usually prose, although developers can request JSON or tool calls.
That flexibility is valuable when the correct answer is not known in advance. It also creates extra engineering work when a program must rely on the answer: developers have to parse the response, handle missing or unexpected fields, validate claims, and decide what to do when the model is uncertain.
Decision-1 moves those constraints to the input design. You define labels, criteria, thresholds, and fallback behavior first. The model then operates inside that contract.
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| Dimension | Decision-1 | Chat interface |
|---|---|---|
| Output | Choice, score, probability, or rubric result | Generated language, with optional structured formatting |
| Best task shape | Fixed labels, thresholds, rankings, gates, and routes | Dialogue, explanation, synthesis, brainstorming, and clarification |
| Who defines the answer space | The product or workflow designer | The user and model discover it during conversation |
| Typical user experience | Invisible orchestration behind a form, app, or agent | Visible conversational interaction |
| Application integration | Directly branch on a known value or probability | Parse and validate generated text or tool calls |
| Uncertainty handling | Use probabilities and explicit thresholds | Infer uncertainty from language or request another answer |
| Primary setup work | Define options, criteria, labels, and fallbacks | Design prompts, conversation state, and response handling |
| Latency and price | Microsoft reports very low latency and input-only pricing | Varies by model, context length, and output generated |
When Decision-1 is the better choice
Routing and intent classification
If every request must be sent to one of a known set of queues, a decision model can return the queue name and confidence directly. Examples include sending a billing issue to finance support, a suspected outage to incident response, or a coding question to the correct specialist agent. A probability threshold can keep low-confidence cases out of automatic routing.
Eligibility checks and workflow gates
Decision-1 can evaluate whether a case passes a defined rule or whether an agent may perform its next action. The surrounding application should still enforce hard business rules in code; the model is useful for interpreting unstructured text or evidence before those rules run.
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Ranking and next-action selection
When a system has a finite list of candidates, the model can score or select among them. This fits recommendations with a constrained catalog, search-result relevance, skill-based assignment, and prioritization of incoming work.
Evaluation, labeling, and safety screening
The structured API supports rubric grading of AI responses and agent actions. It can label data, judge whether content meets a safety policy, validate fields, or decide whether a computer or user-interface action is acceptable. The rubric must be explicit enough that two reviewers would apply it consistently.
Operational control
Because the result is designed for software, Decision-1 is useful as an invisible control layer. It can decide which model to call, which skill an agent should use, whether a request needs human review, or which robot or interface action is available next.
When chat works better
Exploring ambiguous preferences
A user deciding between travel plans, software approaches, or product configurations may not know the relevant criteria at the start. Chat can ask, “Is price or battery life more important?” and update the recommendation as the user answers. A fixed-choice model cannot discover missing criteria unless the interface collects them first.
Explaining trade-offs
Decision-1 can select “Option B,” but its contract is a decision, not a user-facing explanation. Chat is better for describing why an option fits, what compromises it makes, and what would change the recommendation.
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Follow-up questions and revision
Conversation is the right interface when the user expects to challenge an answer, provide new evidence, or ask for a different level of detail. A chat model can preserve the thread and reformulate the result without forcing the user through a new form.
Open-ended synthesis
Summarizing several documents, drafting a plan, comparing unfamiliar alternatives, or brainstorming possibilities does not naturally begin with a fixed label set. Chat can first create a candidate set; a decision model can then rank or filter that set if the workflow eventually needs a controlled choice.
What the published benchmarks show
Microsoft reports the following results in its 2026 materials. They are Microsoft benchmarks or internal tests, not independent replications:
| Reported result | What it measures | Qualification |
|---|---|---|
| Highest accuracy across nearly 150,000 questions and 36 benchmarks | Overall comparison with other models | Microsoft’s comparison; no independent verification was provided in the 2026 Microsoft materials |
| About 35× faster median latency than GPT-6 Sol | Response speed | Microsoft-reported 2026 figure; conditions and hardware are not specified here |
| 1.3% average decision flips under eight perturbations | Stability when prompts or option order change | Microsoft reports zero flips when option descriptions were paraphrased or options were reversed or shuffled |
| More than 10,000 Xbox feedback and review items | Theme extraction from real-world feedback | Microsoft says quality was competitive with GPT-6 Sol, more than 14× faster, and 200× less expensive |
| $0.042 per million input tokens; output tokens free | Published input pricing | Microsoft’s stated price; confirm current regional and account terms before deployment |
These figures support a clear hypothesis: a small, specialized decision model can be fast and inexpensive when the job is constrained. They do not prove that it is better for every recommendation or that it will outperform a chat model on your labels, language, domain, or edge cases. Build an evaluation set from your own traffic before replacing an existing system.
Can Decision-1 explain its choices?
Not in the same way a chat model can. Decision-1’s native product is a structured result: an option, score, or probability. A probability tells your application how strongly the model favored an option; it is not a human-readable chain of reasoning and should not be presented as proof that the choice is correct.
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To give users an explanation, pass the structured result, the relevant evidence, and the applicable policy to a chat model. Instruct the chat layer to explain the selected option, state meaningful uncertainties, and avoid inventing evidence. Store the decision and explanation as separate records so that a later wording change does not alter the auditable decision.
A hybrid design that combines both interfaces
The hybrid pattern keeps deterministic workflow control separate from natural-language interaction:
- Collect state and constraints. Use form fields, account data, documents, or an initial chat turn to capture the facts the decision requires.
- Define the answer space. Give Decision-1 named options, a concise description of each, and the rubric or criteria used to distinguish them.
- Run the decision. Use the result for intent classification, eligibility, candidate ranking, or next-action selection.
- Apply thresholds. Auto-route high-confidence results. Send borderline results to human review or a chat model for clarification. Keep a separate “unknown” or “needs review” path rather than forcing a guess.
- Explain and continue. Have chat present the selected option in plain language, answer follow-up questions, and collect missing information.
- Log separately. Record the option set, model version, input facts, probabilities, threshold, final action, and explanation text as distinct fields.
For example, a support application could classify an incoming message into billing, account access, outage, or other. High-confidence cases go straight to the corresponding queue. A low-confidence case opens chat, which asks one or two targeted questions; the revised facts are then sent through the decision step again.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to define a reliable Decision-1 contract
Keep labels mutually understandable
Options should differ by decision-relevant criteria, not by vague wording. “Urgent,” “important,” and “high priority” overlap unless the rubric defines them. Include an explicit fallback for cases that fit none of the labels.
Separate evidence from the action
Provide the facts the model may consider, then define what each option means operationally. Do not hide a business rule inside an ambiguous label. If a legal, financial, or security rule is absolute, enforce it in application code as well.
Calibrate thresholds on real examples
A probability of 0.80 is not automatically safe for every workflow. Measure precision, recall, false approvals, false rejections, and the cost of human review on a labeled sample. Choose a threshold separately for each route if the consequences differ.
Test perturbations and drift
Rephrase option descriptions, reorder the choices, vary spelling and formatting, and include incomplete or adversarial inputs. Microsoft reports low flip rates under eight perturbations, but your own labels can still be sensitive to wording. Re-run the test when the model, rubric, data distribution, or policy changes.
Design the fallback before launch
Every automated decision needs an outcome for uncertainty, missing data, service failure, and conflicting evidence. A safe fallback may be a human queue, a clarifying question, or a read-only response rather than an irreversible action.
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Latency and cost: which is faster and cheaper for routing?
For constrained routing, Microsoft positions Decision-1 as both faster and cheaper. Its published price is $0.042 per million input tokens, with output tokens free. Microsoft also reports approximately 35 times lower median latency than GPT-6 Sol in its 2026 comparison. In the Xbox feedback test, Microsoft says Decision-1 was over 14 times faster and 200 times less expensive while producing competitive theme quality.
Those numbers describe Microsoft’s measurements, not a universal guarantee. Actual latency includes network time, queueing, prompt length, retries, and any second model used for explanation. A hybrid design can also cost more than a decision-only pipeline because uncertain cases invoke chat. Calculate cost per completed workflow, including review and retries, rather than comparing token prices alone.
Common failure modes and fixes
- Forcing an open-ended problem into fixed labels: allow a chat discovery step or add a review path.
- Overlapping options: rewrite the rubric with observable distinctions and examples.
- Using probabilities as certainty: calibrate against labeled outcomes and set conservative thresholds.
- Showing raw model output to users: use chat or curated templates for explanations.
- Letting the model enforce hard policy: duplicate non-negotiable rules in deterministic code.
- Ignoring option-order effects: test shuffled and paraphrased descriptions before release.
- Logging only the final prose: retain the structured decision, probabilities, evidence, and model version.
- Assuming benchmark results transfer directly: evaluate your language, domain, labels, and error costs.
A decision framework for product teams
| Question | Choose Decision-1 when… | Choose chat when… |
|---|---|---|
| Are valid outcomes known in advance? | Yes, and the application can enumerate them | No, or the user must help discover them |
| Must code act immediately? | A branch, threshold, queue, or tool call follows the result | A person mainly needs an explanation or draft |
| Is uncertainty measurable? | You can label outcomes and set review thresholds | The task depends on nuanced judgment that changes during dialogue |
| Will users ask “why?” | Use Decision-1 for selection and add a chat explanation layer | Use chat as the primary interface |
| Is speed or unit cost dominant? | Short, repeated classifications or gates favor the specialized model | Long context, synthesis, and multi-turn interaction justify chat |
The simplest rule is to classify the operation, not the brand of model. If the next line of code is “if option equals X, do Y,” a decision model is a natural fit. If the next line is “ask what the user means and explain the alternatives,” chat is the natural fit.
Bottom line for guided choices
Microsoft-Decision-1 is the stronger component for fast, repeatable choices over a defined answer space: routing, classification, ranking, verification, grading, and workflow control. Chat is stronger for preference discovery, explanation, synthesis, and conversational revision. A production-quality guided-choice experience usually uses both: Decision-1 supplies the controlled decision and confidence signal; chat supplies the human explanation and follow-up conversation.
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