Microsoft has a new AI model that it says makes decisions 35 times faster than GPT-6 Sol, and in one real workload cost roughly one-two-hundredth as much. It is called Microsoft-Decision-1, announced on October 9, 2026, and it is already available to developers through Microsoft Foundry and OpenRouter.
Before you get too excited, the headline numbers come with important context. This is not a smarter ChatGPT. It cannot write essays, hold conversations, or answer open-ended questions at all. Instead, it does one narrow thing — picking the best option from a predefined list — and does it extremely fast and cheaply. Every performance figure so far comes from Microsoft’s own testing, which has not yet been independently verified.
Key Takeaways
- Microsoft-Decision-1 claims 35x faster decision-scoring than GPT-6 Sol at roughly 1/200th the cost — but only for fixed-choice tasks, not general chat.
- Priced at $0.042 per million input tokens with free output; available via Microsoft Foundry and OpenRouter.
- All speed and accuracy figures are Microsoft’s own claims and await independent verification.
What Decision-1 actually does
Most AI models you have used generate text one word at a time. Decision-1 works differently. You hand it a situation, a question, and a fixed set of possible answers or actions, and it returns a probability score for each option. The application then decides what to do: proceed with the top choice, retry, or escalate to a human. That makes it a specialist for jobs like:
- Classifying content into predefined categories
- Routing requests to the right department or workflow
- Grading and verifying AI-generated responses
- Prioritizing tasks and controlling multi-step AI agent workflows
It handles yes-or-no questions, multiple-choice selections, rating scales, and rubric-based scoring of AI outputs through a structured API. What it explicitly does not do: open-ended text generation, conversation, translation, or summarization.
The “35x faster” claim, explained
According to Microsoft’s published benchmarks, Decision-1’s median response time was about 35 times lower than GPT-6 Sol’s when both were tested on decision-scoring tasks. A critical reading of Microsoft’s figures puts that at roughly 85 milliseconds versus around 3 seconds for GPT-6 Sol.
Two things matter here. First, this is median latency on a narrow task — choosing between fixed options — not a general speed comparison between AI models. GPT-6 Sol is a large general-purpose model doing far more work per request, so a dramatic gap is expected. Microsoft also reported the model was 4.5 times faster than Quyet-1.0-Large and 2.5 times faster than H2O-Lightning-4B v1.1, its closest specialist rivals.
Microsoft says Decision-1 also achieved the highest accuracy across 36 benchmarks covering nearly 150,000 questions kept separate from its training data. Independent analysts have noted, however, that the company has not published which benchmarks those were or per-benchmark scores — so treat the accuracy crown as a company claim for now, not a settled fact.
The “200x cheaper” claim, explained
The eye-catching cost figure does not come from a price list. It comes from a real internal test: Microsoft’s Xbox Research team used Decision-1 to sort more than 10,000 player reviews from surveys, Steam, and X into predefined topics. The company says the quality was comparable to GPT-6 Sol’s, while processing ran more than 14 times faster at roughly one-two-hundredth of the cost.
That is a genuine result on a genuine workload — but it is one workload, and review classification is exactly the kind of repetitive, fixed-choice task Decision-1 was built for, so it is the friendliest possible comparison.
The actual pricing is public and straightforward: $0.042 per million input tokens, with output tokens completely free. Because the model only returns short scores rather than generated text, most of what you pay for is what you send in.
Built on borrowed foundations
Decision-1 is not built on a Microsoft-designed base model. It was created by post-training Alibaba’s open Qwen3.5-9B model for single-pass decision scoring. Microsoft says future versions will be rebased onto other foundations, including its own MAI models and OpenAI’s models — a sign that the competitive edge is shifting from who trains the biggest base model to who tunes models best for specific, high-volume jobs.
Microsoft is already using it on itself
The most convincing evidence may be Microsoft’s own dogfooding. Beyond the Xbox review-sorting test, the company reports its Copilot team used the model to grade chat and agent responses with results competitive with GPT-5.6 Luna at 100 times the speed. In incident-response tests, it retrieved relevant information better and faster than the large language model it was compared against. And in Microsoft Discovery, where AI agents evaluate experiment results and plan next steps, it produced scores with 46 times greater consistency than LLM-based scoring while running three times faster.
A bigger shift is underway
In the same week as Microsoft’s launch, OpenAI made its Decisions API generally available, powered by GPT-6 Luna — an API that returns structured answers instead of generated text, which OpenAI says runs up to 10 times faster than GPT-6 Luna through its standard API. OpenAI prices it at $0.10 per million input tokens, which makes Microsoft’s $0.042 look deliberately aggressive. The industry message is consistent: the next AI battleground is not which model is smartest, but which one gets routine work done fastest and cheapest.
What this means for developers in Pakistan
For Pakistani developers, startups, and freelancers building AI-powered products, the practical takeaway is about economics. AI agent workflows — customer support bots, content moderation pipelines, document triage — often burn most of their API budget on thousands of small classification and routing decisions, not on the final answer. A model that handles those micro-decisions at a fraction of the cost could change what is viable to build on a tight budget.
Since Decision-1 is available through OpenRouter, which many independent developers already use as a single gateway to multiple models, experimenting with it does not require an Azure enterprise contract. The sensible move, though, is the one Microsoft itself recommends: validate the model and its confidence thresholds on examples from your own workload before trusting it in production.
The fine print
Three caveats deserve emphasis. First, all speed, accuracy, and cost figures come from Microsoft’s own evaluations; no independent lab has replicated them yet. Second, this model cannot replace a general-purpose LLM — any task requiring actual reasoning, writing, or conversation still needs the big models. Third, decision models can be sensitive to how options are phrased and ordered.
Bottom line
Microsoft-Decision-1 is a real, available product addressing a real bottleneck: the cost and latency of the thousands of small decisions inside modern AI systems. The 35x speed and 200x cost figures are genuine company-reported results on specific decision-scoring workloads — not marketing fiction, but not universal truths either. If independent testing confirms even a fraction of the claimed advantage, the era of the specialist AI model has clearly begun.
