01What Microsoft Decision-1 Is and How It Works

Microsoft Decision-1 is a new AI model from Microsoft designed to make decisions faster than traditional generative models. Instead of writing paragraphs or essays, it takes a situation and a fixed list of choices, then scores each option in one pass. This gives you a quick, structured output that other systems can act on right away.

Most AI models are built to talk, explain, or generate text. Decision-1 does something different. You give it a situation and a set of choices, and it returns a score next to each option.

No extra words, just a decision. This makes it ideal for systems that need to act immediately without waiting for a long response.

Microsoft reports that Decision-1 has 35 times faster median latency than GPT-6 sole in their benchmarks. Their tests covered 36 benchmark suites and nearly 150,000 questions, and Decision-1 came out on top for accuracy across all of that. These are Microsoft's own reported figures, so keep that in mind.

02Main Features and What's New with Decision-1

Decision-1 brings several new features that set it apart from other AI models. The core feature is its ability to score choices quickly without generating long text. This makes it a lightweight decision layer that can fit into any AI agent workflow.

  • Fast scoring: It scores options in milliseconds, reducing latency at every decision point.
  • Structured output: It returns simple scores, so systems can act on them immediately.
  • High accuracy: Microsoft's tests show it outperforms other models across 36 benchmark suites.
  • Scalable: It can handle thousands of tasks, making it suitable for large-scale operations.
  • Available now: It is live on Microsoft Foundry and Open Router for testing.

These features make Decision-1 a valuable tool for anyone building AI agents that need to make quick, accurate decisions. The shift from generation to scoring is significant because it saves time and tokens at each step.

03How to Set Up and Get Started with Decision-1

Getting started with Microsoft Decision-1 is straightforward. Here are the steps to set it up and begin using it in your workflows.

  1. Visit Microsoft Foundry or Open Router to access Decision-1.
  2. Check the official documentation for detailed instructions on using the model.
  3. Prepare your data: define a situation and a fixed list of choices for your use case.
  4. Test Decision-1 on your own data to see how it scores different options.
  5. Integrate it into your AI agent workflow by connecting the scoring output to your automation stack.
  6. Start with a small pilot, like classifying posts or routing requests, to measure performance.

Once you have it running, you can expand to more complex tasks. The key is to start simple and validate the results before scaling up.

04How to Use Decision-1 with Real Examples

Using Decision-1 is practical and simple. In this episode, Julian Goldie gave a concrete example of an AI automation community. Thousands of people post questions daily, and someone must decide where to route each post.

You can build a classifier with Decision-1. Give it the post and a fixed list of categories like support, onboarding, advanced training, or general discussion. Decision-1 scores each option in milliseconds, and the highest-scoring category triggers the right response or routing rule.

This removes the need for a heavy generative model to spend half a second on each post.

Another example is from Microsoft Discovery. They used Decision-1 to evaluate experiments and replan. It gave them 46 times greater consistency in scoring and nearly 4 times faster adaptive re-planning.

For workflows that run hundreds of cycles, this makes a big difference.

You can also use Decision-1 for quality control, incident response, or any task with bounded choices. Just structure your input and options, and let Decision-1 handle the scoring.

05Results and Tests Shown in the Episode

Microsoft's reported results for Decision-1 are impressive. In their own benchmarks, Decision-1 was 35 times faster in median latency than GPT-6 sole. Their evaluation covered 36 benchmark suites and nearly 150,000 questions, and Decision-1 came out on top for accuracy.

Real-world internal use cases also show strong results:

  • Xbox Research: Categorized over 10,000 pieces of player feedback, over 14 times faster than the comparison model, with quality held up.
  • Co-pilot team: Used Decision-1 for quality control, achieving 100 times faster processing compared to their baseline.
  • Incident response team: Saw better and faster knowledge retrieval.
  • Microsoft Discovery: Experienced 46 times greater consistency in scoring, 3 times faster scoring, and nearly 4 times faster adaptive re-planning.

These numbers show how Decision-1 can change system design by reducing latency and improving consistency at scale.

06Tips, Limits, and Mistakes to Avoid with Decision-1

When using Decision-1, there are some tips and limits to keep in mind. First, remember that the reported figures are from Microsoft themselves. They ran the tests, so consider the source when evaluating performance.

Here are some tips to get the most out of Decision-1:

  • Define your choices clearly. Bounded choices work best for a decision model.
  • Use it for classification or routing tasks, not for open-ended generation.
  • Start with a pilot to test accuracy on your specific data.
  • Combine it with a generative model for tasks that need both decision and explanation.
  • Avoid using it for tasks that require unstructured responses.

A common mistake is expecting Decision-1 to write or explain. It is built for scoring, so keep your use case focused. Also, don't rely solely on Microsoft's numbers; test it on your own data to see real performance.

07Who Should Use Decision-1 and Why It Matters

Microsoft Decision-1 is for anyone building AI agents or automation workflows. It matters because fast, accurate decisions enable systems to run more loops and handle more complexity. This is key for scaling AI in business.

Business owners who are still using one big AI model for everything might find their workflows slow and expensive. Decision-1 offers a way to speed up decision points and reduce costs. It is especially useful for teams handling large volumes of tasks, like support routing or content categorization.

In this episode, Julian Goldie highlighted that most people haven't figured out how to use specialist models yet. The ones who do will get ahead. Decision-1 is available now, so you can start testing it and thinking about where a fast decision layer could speed up your workflows.