Microsoft separates decision-making from generative AI with a new model
InfoWorld ·
Microsoft has launched Decision-1, an AI model that returns probabilities for predefined answers, allowing software applications to use its outputs directly instead of processing a free-form response from a large language model (LLM). The model targets tasks such as classifying requests, prioritizing incidents, and checking AI-generated responses. Microsoft says it can handle these decisions with lower costs and latency than approaches that rely on general-purpose LLMs. “Instead of writing a free-form response, it takes a fixed set of answer options and returns a calibrated probability for each one in a single pass,” Microsoft said in a blog post. Microsoft-Decision-1 is available through Microsoft Foundry and OpenRouter, according to the company. Microsoft built the model by post-training Qwen3.5-9B and said it plans to rebase it on other models, including Microsoft AI (MAI) and OpenAI models. Model targets specific decisions Decision-1 supports yes-or-no judgments, choices among predefined options, ratings, and rubric-based assessments of AI responses and agent actions, Microsoft said. Developers can use its outputs to classify inputs, rank work, verify responses, or determine the next action in an AI agent workflow. Unlike a general-purpose LLM, which can generate explanations and other natural-language content, decision models return probabilities for the available answers. Microsoft said its Decision-1 model can also evaluate multiple decisions in parallel within a single request. Microsoft cites benchmark results According to Microsoft, Decision-1 achieved the highest accuracy in a comparison of 36 benchmarks covering nearly 150,000 questions kept blind from training. The company also reported that the model was about 2.5 times faster than the runner-up and 35 times faster than GPT-6 Sol. Microsoft lists the price at 4.2 cents per million input tokens, with no charge for output tokens, according to the blog post. In an incident-prioritization example, the company said a request using 86 input tokens and two output tokens cost approximately $0.0000036. At that rate, $1 would cover more than 275,000 similar decisions. The benchmark results and cost calculations are Microsoft’s claims and have not been independently verified. Charlie Dai, vice president and principal analyst at Forrester, said specialized decision models could reduce the cost and latency of agentic AI workflows. “Routing, classification, and approval checks often represent a large share of agent interactions, so a specialized decision model can significantly reduce cost and latency while reserving general-purpose LLMs for reasoning and content generation,” Dai said. Amit Kumar Jena, development manager for AI at Kanerika, said moving routine routing and approval checks away from large generative models represents a meaningful architectural change for enterprises scaling AI agents. However, Jena cautioned against using Microsoft’s benchmark results to predict enterprise savings. He said organizations should measure “cost and latency per successfully completed workflow, including fallbacks and mistakes.” Governance risks Decision-1 returns scores but does not provide a rationale for each decision. Microsoft’s documentation also warns that results can change depending on how questions are worded, Jena said. “My concern is that a precise-looking probability can give an uncertain decision more authority than it deserves,” he said. Dai said the absence of reasoning can make decisions harder to explain, validate and challenge. He recommended calibration testing, controls over question wording and score thresholds, audit logging, and human review for high-impact decisions, particularly when customer outcomes or regulatory obligations are involved. Jena recommended beginning with reversible tasks, such as request routing and triage, and requiring meaningful human review for decisions that materially affect people. The concerns center on how organizations interpret the model’s scores and use them to trigger actions. A probability assigned to an answer does not, by itself, explain the decision or establish that it is correct for a particular enterprise application.
Microsoft has launched Decision-1, an AI model that returns probabilities for predefined answers, allowing software applications to use its outputs directly instead of processing a free-form response from a large language model (LLM). The model targets tasks such as classifying requests, prioritizing incidents, and checking AI-generated responses. Microsoft says it can handle these decisions with lower costs and latency than approaches that rely on general-purpose LLMs. “Instead of writing a free-form response, it takes a fixed set of answer options and returns a calibrated probability for each one in a single pass,” Microsoft said in a blog post. Microsoft-Decision-1 is available through Microsoft Foundry and OpenRouter, according to the company. Microsoft built the model by post-training Qwen3.5-9B and said it plans to rebase it on other models, including Microsoft AI (MAI) and OpenAI models. Model targets specific decisions Decision-1 supports yes-or-no judgments, choices among predefined options, ratings, and rubric-based assessments of AI responses and agent actions, Microsoft said. Developers can use its outputs to classify inputs, rank work, verify responses, or determine the next action in an AI agent workflow. Unlike a general-purpose LLM, which can generate explanations and other natural-language content, decision models return probabilities for the available answers. Microsoft said its Decision-1 model can also evaluate multiple decisions in parallel within a single request. Microsoft cites benchmark results According to Microsoft, Decision-1 achieved the highest accuracy in a comparison of 36 benchmarks covering nearly 150,000 questions kept blind from training. The company also reported that the model was about 2.5 times faster than the runner-up and 35 times faster than GPT-6 Sol. Microsoft lists the price at 4.2 cents per million input tokens, with no charge for output tokens, according to the blog post. In an incident-prioritization example, the company said a request using 86 input tokens and two output tokens cost approximately $0.0000036. At that rate, $1 would cover more than 275,000 similar decisions. The benchmark results and cost calculations are Microsoft’s claims and have not been independently verified. Charlie Dai, vice president and principal analyst at Forrester, said specialized decision models could reduce the cost and latency of agentic AI workflows. “Routing, classification, and approval checks often represent a large share of agent interactions, so a specialized decision model can significantly reduce cost and latency while reserving general-purpose LLMs for reasoning and content generation,” Dai said. Amit Kumar Jena, development manager for AI at Kanerika, said moving routine routing and approval checks away from large generative models represents a meaningful architectural change for enterprises scaling AI agents. However, Jena cautioned against using Microsoft’s benchmark results to predict enterprise savings. He said organizations should measure “cost and latency per successfully completed workflow, including fallbacks and mistakes.” Governance risks Decision-1 returns scores but does not provide a rationale for each decision. Microsoft’s documentation also warns that results can change depending on how questions are worded, Jena said. “My concern is that a precise-looking probability can give an uncertain decision more authority than it deserves,” he said. Dai said the absence of reasoning can make decisions harder to explain, validate and challenge. He recommended calibration testing, controls over question wording and score thresholds, audit logging, and human review for high-impact decisions, particularly when customer outcomes or regulatory obligations are involved. Jena recommended beginning with reversible tasks, such as request routing and triage, and requiring meaningful human review for decisions that materially affect people. The concerns center on how organizations interpret the model’s scores and use them to trigger actions. A probability assigned to an answer does not, by itself, explain the decision or establish that it is correct for a particular enterprise application.