Microsoft-Decision-1

Microsoft-Decision-1 is a new decision-scoring model from Microsoft, available in Microsoft Foundry and through OpenRouter. It is purpose-built to deliver structured outputs that software can immediately act on, and it is designed for routing, classification, prioritization, verification, and workflow control. The model is intended to make it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Give Microsoft-Decision-1 a set of fixed options and it returns a calibrated probability for each option in a single pass, as structured output that software can act on directly. It is positioned as a model for fast decision-making, delivering top performance in latency and quality on structured decision tasks to outperform both LLMs and other decision models. Decision models are quickly emerging as an important new category in AI. 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. Microsoft frames this as a shift: once you understand the capability of making decisions and classifying things at very low cost with high performance, all kinds of useful tasks get unlocked. Traditional large language models are oriented toward generation and reasoning rather than acting as reliable decision engines, and Microsoft-Decision-1 is designed specifically for that gap. By focusing on structured decision tasks rather than open-ended text generation, the model aims to outperform both LLMs and other decision models on latency and quality, so teams do not have to choose between the flexibility of a general model and the reliability of a purpose-built decision component. Accuracy is a central claim for Microsoft-Decision-1. Microsoft reports that the model achieved the highest accuracy in its 36-benchmark comparison, spanning nearly 150,000 questions across benchmarks kept blind from training. The accuracy figure is reported as a mean score across 36 benchmarks, covering 147,137 questions. This breadth — dozens of separate benchmarks and well over a hundred thousand questions — is intended to demonstrate that the model's strength is not limited to a single task type but holds across a wide range of structured decision problems. For teams evaluating a model for routing or classification, that breadth matters because it suggests the model can be trusted across varied inputs rather than only on a narrow distribution of examples. Microsoft also notes that Strands-Decider 2B was scored as the average of the 23 of 36 benchmarks it could answer, illustrating how comparisons were handled for models that could not complete the full benchmark set. Latency is the second headline capability. Microsoft states that in its benchmarking, Microsoft-Decision-1 was the fastest measured, running 2.5 times quicker than H2O-Lightning-4B v1.1, the runner-up, and 35 times quicker than GPT-6 Sol. The latency figures are described as JevBench v1.6.1 adjusted median (p50), checked on October 7, 2026, with Microsoft-Decision-1 measured through Foundry in the same region. The Product Hunt listing echoes this with the claim that the model is about 35 times faster than GPT-6 Sol at median latency. Speed matters for decision tasks because routing, classification, verification, and workflow control often sit directly in the critical path of an application or agent loop. When a decision must be made on every request, a model that returns answers in a fraction of the time can change what is practical to build — for example, making it feasible to score many inputs cheaply and quickly instead of batching decisions or skipping them altogether. Calibration is the third dimension Microsoft highlights. In the benchmark comparison, Microsoft-Decision-1 placed third on calibration, 1.5 behind Jev. Calibration is defined in the source material as a measure where 100 means a model's confidence exactly matches how often it is right — in other words, a well-calibrated model's stated probability reflects its true likelihood of being correct. Because Microsoft-Decision-1 returns a calibrated probability for each fixed option in a single pass, downstream software can act on those probabilities directly, for example by applying thresholds, comparing options, or routing based on confidence. The model produces structured output that software can act on directly, rather than free-form text that must be parsed or interpreted. That combination — calibrated probabilities delivered as structured output in a single pass — is what makes the model suitable for use as a decision component inside larger systems. How Microsoft-Decision-1 works overall can be summarized from the source material: it is a decision-scoring model that is post-trained from Qwen3.5-9B. Rather than generating prose, it is given a fixed set of options and returns a calibrated probability for each option in a single pass, as structured output. This single-pass, fixed-option approach is the model's distinctive methodology. The model is available in Microsoft Foundry and through OpenRouter, so teams can access it through those platforms rather than deploying a bespoke system. Microsoft describes it as delivering top performance in latency and quality on structured decision tasks, and the benchmarking data presented alongside the launch compares it against both LLMs and dedicated decision models on accuracy, median latency, and calibration. For users, the stated benefits center on speed, cost, and reliability of decisions. Microsoft says the model makes it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Because it returns calibrated probabilities that software can act on directly, it reduces the need to translate model output into actionable signals. The combination of high accuracy across a broad benchmark set, the fastest measured median latency, and calibrated confidence is intended to give teams confidence when embedding the model in automated decision paths. Microsoft also emphasizes low cost: making decisions and classifying things at very low cost with high performance is framed as the capability that unlocks useful tasks. On Product Hunt, the model is listed at $0.042 per million input tokens, with free output. The source material names specific use cases for Microsoft-Decision-1: routing, classification, prioritization, verification, and workflow control. It is also described as a model for agents and workflows, and more broadly as a decision model for agent control. These use cases share a common shape — they are structured decision tasks where a system needs to choose among a fixed set of options and act on the result. Routing describes directing a request, item, or task to the right destination or handler. Classification describes assigning items to categories. Prioritization describes ranking or ordering work. Verification describes checking whether something meets a condition. Workflow control describes deciding how a process should proceed. In each case, the model returns calibrated probabilities for the available options in a single pass, giving the surrounding application or agent a structured signal it can act on. Microsoft-Decision-1 is aimed at teams that want to incorporate decision intelligence into existing applications, agents, and workflows. Its intended environments are those where a secure, trusted model of this kind is valuable. It is available in Microsoft Foundry and through OpenRouter, giving teams two routes to access it. The model is post-trained from Qwen3.5-9B, which is the base model identified in the source material. Pricing is described on Product Hunt as $0.042 per million input tokens, with free output, and the model is presented as making decisions and classifying at very low cost. The benchmarking in the launch material reports accuracy as a mean score across 36 benchmarks (147,137 questions), latency as JevBench v1.6.1 adjusted median (p50) checked October 7, 2026, and calibration on a scale where 100 means confidence exactly matches how often the model is right. Taken together, Microsoft-Decision-1 represents Microsoft's entry into the emerging category of decision models — models purpose-built to deliver structured outputs that software can immediately act on. Its value proposition rests on three measured dimensions: the highest accuracy in Microsoft's 36-benchmark comparison spanning nearly 150,000 questions, the fastest measured latency (2.5 times quicker than the runner-up H2O-Lightning-4B v1.1 and 35 times quicker than GPT-6 Sol), and competitive calibration (third, 1.5 behind Jev). By returning calibrated probabilities for fixed options in a single pass as structured output, and by being available in Microsoft Foundry and through OpenRouter, it gives teams a practical way to add fast, reliable decision-making to their applications, agents, and workflows.