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Microsoft Decision-1 is built to score a fixed set of choices, not to write an explanation. That makes it a specialized option for ranking, routing, and other structured decisions; a general-purpose small language model may be a better fit when you also need prose, summaries, or generated content. Microsoft reports strong benchmark results for Decision-1, but those vendor figures do not establish that it will rank choices best for your workload. Compare it against alternatives using the same examples, labels, and deployment conditions.
What Microsoft Decision-1 does differently
Microsoft announced Decision-1 on October 9, 2026, describing it as a model created by post-training Qwen3.5-9B for fast, single-pass decision scoring. Given a fixed set of answer options, it returns probability scores. Microsoft lists yes/no decisions, multiple choice, ordered ratings, and rubric-based grading among supported tasks. Potential applications include classification, prioritization, routing, verification, recommendation selection, search relevance, and workflow control. Microsoft’s announcement
Its key trade-off is output. Decision-1 returns a structured judgment that an application can use, but Microsoft Learn says it does not produce free-form responses or written rationales. That is useful when the next step is to sort, route, or escalate an item. A generative model is more suitable when the system must also explain its choice, summarize source material, or create content. Microsoft Learn: using Microsoft Decision-1
How to represent a ranking task
Microsoft Learn describes three question types relevant to decisions. For ranking, the important distinction is between choosing among options and assigning an ordered score.
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| Question type | What it returns | When it fits |
|---|---|---|
noul |
A probability for a binary question | A yes/no decision, such as whether an item meets a stated criterion |
choice |
A selected option and a probability for each option | Picking one choice from a fixed list |
score |
A position on an ordered scale, calculated as a probability-weighted average of the level indexes | Ordering items or applying a rubric with ordered levels |
For relative ordering, Microsoft recommends using scores to compare items and set thresholds, rather than treating a score as an absolute, calibrated rating. The scale should describe the decision clearly: for example, levels might represent increasing relevance to a specific query, not an undefined notion of “quality.” Microsoft Learn: using Microsoft Decision-1
What Microsoft’s performance claims show—and don’t show
Microsoft reports that Decision-1 had the highest accuracy across 36 benchmarks and nearly 150,000 questions kept blind from training. The company also reports that it was 2.5 times quicker than H2O-Lightning-4B v1.1, the runner-up in the displayed comparison, and 35 times quicker than GPT-6 Sol. These are Microsoft-reported benchmark results, not an independent, like-for-like evaluation of the small models you might use for your own ranking task. The announcement was updated to add Jev accuracy and calibration benchmarks, so the figures refer to Microsoft’s currently displayed comparison. Microsoft’s announcement
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Microsoft also reports a robustness test with a 1.3% average decision change across eight perturbations and zero flips when option descriptions were paraphrased or options were reversed or shuffled. Its safety evaluation covered 5,250 requests across 11 benchmarks for harmful content, jailbreak attempts, and prompt injection. These results describe Microsoft’s tests; they do not guarantee the same stability or safety on different prompts, options, or deployment conditions. Microsoft’s announcement
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One internal example is more specific than a general performance claim: Microsoft says Xbox Research processed more than 10,000 open-ended feedback items with quality competitive with GPT-6 Sol, at over 14 times the speed and 200 times lower cost. Those results apply to that reported example, not automatically to another organization’s data, model setup, or workload. Microsoft’s announcement
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How to compare Decision-1 with other small models
There is no independent, like-for-like comparison establishing how Decision-1 ranks against a representative group of small language models on your particular choice-ranking task. Microsoft Foundry’s general model leaderboards compare quality, safety, cost, and throughput across models, but Microsoft labels the feature preview and cautions that public standardized benchmarks do not replace workload-specific evaluation. The leaderboards can change as models and datasets are updated. Microsoft Foundry model benchmarks
A useful comparison controls the inputs and measures the outcomes that matter to your application:
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- Build a representative test set. Use held-out examples that resemble production inputs, with the same candidate choices, decision criteria, and known outcomes for Decision-1 and each alternative. Keep examples used to tune prompts or settings out of the final evaluation.
- Choose a ranking metric that matches the task. If the application needs the right top choice, measure top-choice accuracy. If the order of several candidates matters, use a ranking-sensitive measure. Review errors by case type, not just as one aggregate score.
- Check calibration and escalation behavior. Compare confidence with actual correctness, then set thresholds based on the cost of false positives and false negatives. Include an option such as “cannot tell” if uncertain cases should go to a person rather than receive a forced ranking.
- Probe sensitivity. Test paraphrased descriptions, reordered options, changed labels, and realistic formatting noise. Compare how often the preferred choice or ranking changes when the underlying decision should not.
- Measure the deployed system. Record end-to-end latency and throughput at expected concurrency, using the deployment type and configuration you intend to operate. Microsoft cautions that deployment type alone does not guarantee lower latency. Microsoft Learn: using Microsoft Decision-1
- Estimate actual workload cost. Use expected request volume and your actual input and output pattern. Foundry’s general leaderboard cost estimate assumes a three-to-one input-to-output token ratio, which may not represent your workload. Microsoft Foundry model benchmarks
- Review deployment and governance fit. Confirm regional availability, data-zone requirements, access controls, safety needs, and monitoring before selecting a model.
Use clear, neutral wording and select decision thresholds according to the consequences of errors. These checks matter for either model type: a structured probability is only useful if it supports the workflow’s actual decision, and a fluent explanation does not itself establish that a ranking is correct. Microsoft Learn: using Microsoft Decision-1
Limitations and responsible use
Microsoft Learn warns that Decision-1’s scores can change with wording or option order, and that calibration is strongest on familiar task types. The model may rely on outdated knowledge, and it does not explain its judgments. Microsoft also warns that safety filtering may miss subtle harmful content or flag benign content. Microsoft Learn: using Microsoft Decision-1
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For credit, employment, housing, healthcare, or legal decisions, Microsoft says to use the model as decision support with meaningful human review—not as the sole decision-maker. Avoid unnecessary sensitive attributes, monitor errors and unfair disparities, and tell affected people when AI contributes to a decision. Microsoft Learn: using Microsoft Decision-1
Availability and deployment
Microsoft says Decision-1 is available through Microsoft Foundry and identifies OpenRouter as another access route. Microsoft Foundry documentation lists model version 1 and GlobalStandard deployment, with DataZoneStandard in selected regions. Foundry deployment requires an Azure subscription, project access, deployment permissions, and authentication. Check availability and deployment conditions for the account and region you plan to use, since they can change. Microsoft’s announcement; Microsoft Learn: using Microsoft Decision-1
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