Microsoft-Decision-1 is a decision-scoring model for routing, classification, verification and agent control. Give it fixed options and it returns a calibrated probability for each in a single pass, as structured output software can act on directly. Post-trained from Qwen3.5-9B, it is about 35x faster than GPT-6 Sol at median latency, per Microsoft. Available now in Microsoft Foundry at $0.042 per million input tokens, with free output.
Meet Microsoft-Decision-1 from Microsoft: a model built for fast, structured decisions, not text generation. You give it a situation and fixed options, and it returns a calibrated probability for each one in a single pass, so your software can act on the answer directly.
You now get:
Probabilities for yes/no, multiple-choice and rating options, with no text to parse
Rubric-based grading of AI responses and agent actions, through a structured API call
A small, fast model post-trained from Qwen3.5-9B for decision scoring, which Microsoft says it will rebase on other models, including Microsoft AI (MAI) and OpenAI models
What’s new?
Built for decisions, not chat: LLMs generate text or reason through complex problems. Decision-1 handles routing, classification, prioritization, verification and workflow control, where the answer is a choice among known options.
Speed that adds up: Microsoft’s example is that 100 ms added to each of 20 sequential decisions adds two seconds to a workflow. In its tests, Decision-1 is 4.5x faster than the runner-up (Quyet-1.0-Large) and about 35x faster than GPT-6 Sol at median latency.
Accuracy and generalization: Microsoft reports the highest accuracy in a 36-benchmark comparison covering nearly 150,000 questions kept blind from training, and the best results on JevBench across 36 additional public and private benchmarks.
Consistent decisions: Across eight types of input changes, it changes its decision on 1.3% of cases on average, and never when option descriptions are paraphrased or options are reversed or shuffled.
Safety: It was tested on 5,250 requests across 11 benchmarks (harmful content, jailbreaks, prompt injection), and Microsoft says it refused harmful behavior while keeping high utility.
Where Microsoft is already using it:
Xbox Research labeled more than 10,000 pieces of feedback at quality competitive with GPT-6 Sol, over 14x faster and 200x cheaper
The Copilot team saw quality competitive with GPT-5.6 Luna at 100x the speed
Microsoft Discovery got scores 46x more consistent than the LLM-based version, at three times the speed
Listed use cases: agent controls, model routing, data labeling, AI judging, intent analysis, incident routing, data validation, search relevance, content filtering, code scanning, safety screening, computer use, robotics and scientific discovery.
Pricing and availability:
Available now in Microsoft Foundry
OpenRouter is coming soon
$0.042 per million input tokens, and output tokens are free
the part that worries me is "calibrated" becoming a permanent label instead of a measurement. calibration is true against the distribution you tested it on, and production traffic drifts from that distribution constantly. if my code is acting directly on the probability with no human reading the text first, I have no way to notice the moment the calibration quietly stops holding, I just get confidently wrong numbers that look exactly like confidently right ones. is there a way to monitor calibration drift in production, or is re-validating against a fresh labeled sample something teams are expected to build themselves
Interesting price point there m$ 😸 Exactly the same as JEV. Both at $0.042. And boasting 1.2 points higher accuracy and running roughly 2.8 times faster than TypeSafe AI's Jev model. Your move @Jev
About Microsoft-Decision-1 on Product Hunt
“Microsoft’s decision model for agents and workflows”
Microsoft-Decision-1 launched on Product Hunt on October 10th, 2026 and earned 138 upvotes and 3 comments, placing #4 on the daily leaderboard. Microsoft-Decision-1 is a decision-scoring model for routing, classification, verification and agent control. Give it fixed options and it returns a calibrated probability for each in a single pass, as structured output software can act on directly. Post-trained from Qwen3.5-9B, it is about 35x faster than GPT-6 Sol at median latency, per Microsoft. Available now in Microsoft Foundry at $0.042 per million input tokens, with free output.
Microsoft-Decision-1 was featured in Artificial Intelligence (480.7k followers) on Product Hunt. Together, these topics include over 127.6k products, making this a competitive space to launch in.
Who hunted Microsoft-Decision-1?
Microsoft-Decision-1 was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Microsoft-Decision-1 stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Rohan Chaubey
Meet Microsoft-Decision-1 from Microsoft: a model built for fast, structured decisions, not text generation. You give it a situation and fixed options, and it returns a calibrated probability for each one in a single pass, so your software can act on the answer directly.
You now get:
Probabilities for yes/no, multiple-choice and rating options, with no text to parse
Rubric-based grading of AI responses and agent actions, through a structured API call
A small, fast model post-trained from Qwen3.5-9B for decision scoring, which Microsoft says it will rebase on other models, including Microsoft AI (MAI) and OpenAI models
What’s new?
Built for decisions, not chat: LLMs generate text or reason through complex problems. Decision-1 handles routing, classification, prioritization, verification and workflow control, where the answer is a choice among known options.
Speed that adds up: Microsoft’s example is that 100 ms added to each of 20 sequential decisions adds two seconds to a workflow. In its tests, Decision-1 is 4.5x faster than the runner-up (Quyet-1.0-Large) and about 35x faster than GPT-6 Sol at median latency.
Accuracy and generalization: Microsoft reports the highest accuracy in a 36-benchmark comparison covering nearly 150,000 questions kept blind from training, and the best results on JevBench across 36 additional public and private benchmarks.
Consistent decisions: Across eight types of input changes, it changes its decision on 1.3% of cases on average, and never when option descriptions are paraphrased or options are reversed or shuffled.
Safety: It was tested on 5,250 requests across 11 benchmarks (harmful content, jailbreaks, prompt injection), and Microsoft says it refused harmful behavior while keeping high utility.
Where Microsoft is already using it:
Xbox Research labeled more than 10,000 pieces of feedback at quality competitive with GPT-6 Sol, over 14x faster and 200x cheaper
The Copilot team saw quality competitive with GPT-5.6 Luna at 100x the speed
Microsoft Discovery got scores 46x more consistent than the LLM-based version, at three times the speed
Listed use cases: agent controls, model routing, data labeling, AI judging, intent analysis, incident routing, data validation, search relevance, content filtering, code scanning, safety screening, computer use, robotics and scientific discovery.
Pricing and availability:
Available now in Microsoft Foundry
OpenRouter is coming soon
$0.042 per million input tokens, and output tokens are free
Try it: Microsoft Foundry · Launch post