Microsoft Decision-1: 35x faster than GPT-6 Sol for routing (Oct 9, 2026)

On October 9, 2026, Microsoft launched Decision-1 — a specialized AI model for structured decision and classification tasks, post-trained from Alibaba's open-weight Qwen3.5-9B (~9 billion parameters). Microsoft claims Decision-1 is 35× faster than OpenAI's GPT-6 Sol on P50 latency for routing/classification workloads, priced at $0.042 per million input tokens with free output.

The launch signals a broader shift in enterprise AI: instead of asking a giant general-purpose LLM to route, classify, or score, companies are now deploying small, specialized "decision models" for high-volume production workloads — joining recent releases from Liquid AI, Amazon, Cloudflare, and Databricks.

Why this matters

Routing and classification is the most common AI workload in production agent systems — and the most expensive if you use a frontier model. Every tool-call decision, every prompt classifier, every quality-control check, every rubric-based grader requires a model call. Routing these to a 200B+ parameter model burns budget and adds latency.

Microsoft Decision-1's bet: a 9B post-trained model can hit >95% of frontier accuracy for structured decisions, while delivering 35× faster response times and 6× lower cost — making it economically viable to add a decision layer to every workflow.

Result: Enterprise AI pipelines can now run per-request routing decisions at $0.0001 each instead of $0.001+ per GPT-6 call — a 10× cost reduction at scale.

Headline specs

Spec	Decision-1	GPT-6 Sol (baseline)
Architecture	Text-only, scoring model	Text generation model
Base	Qwen3.5-9B (post-trained)	OpenAI frontier model
Parameters	~9B	~200B+ (estimated)
Context window	32K tokens	200K tokens
Output format	Structured JSON decisions	Free-form text
Speed (P50 latency)	~5–8 ms	~250 ms (35× slower)
Decision-flip rate	1.3% (across 8 input perturbations)	Higher variance
Pricing	$0.042/M input, free output	~$0.50/M input + $2/M output

Performance claims

Microsoft's published benchmarks (October 9, 2026 release):

35× faster than GPT-6 Sol (P50 latency, vendor benchmark)
4.5× faster than runner-up Quyet-1.0-Large
2.5× faster than H2O-Lightning-4B v1.1
Topped a 36-benchmark comparison spanning ~150,000 questions
Average decision-flip rate of 1.3% across 8 input perturbations (low = stable)
>90% true-positive rate expected for security scanning (separate OSS Scanner use case)

Important caveat: All performance claims are vendor-reported from Microsoft's own internal benchmarks. No independent third-party evaluation has been conducted yet. As with all "X times faster" claims, real-world performance will depend on hardware, batch size, and workload type.

How Decision-1 works

Unlike a chat model, Decision-1 is purpose-built for structured scoring:

Input: evidence + a closed question + fixed answer options

Output: Calibrated probability scores for each option in one pass (single forward pass)

Architecture:

Text-only input — no image or audio
Returns structured JSON decisions
No free-form text generation — Decision-1 doesn't write essays, it just scores options
32K context window — large enough for typical routing prompts

Example use cases:

Routing: Which agent should handle this user query?
Classification: Is this email spam, urgent, or normal?
Prioritization: Which of these 50 bug reports should we fix first?
Verification: Does this AI-generated answer actually address the user's question?
Quality control: Does this draft meet the rubric?
Agent guardrails: Is this tool call safe to execute?
AI response judging: Which of two model outputs is better?

Pricing

Tier	Input	Output
Microsoft Foundry	$0.042/M tokens	Free
OpenRouter (coming)	TBD	TBD

At $0.042/M input with free output, a typical 500-token routing decision costs ~$0.000021 (less than 1/50th of a cent). For an enterprise running 10M routing decisions per month, that's $420/month in inference — vs. estimated $20,000+/month using GPT-6 Sol for the same workload.

Pricing model: Output is free because the model is producing a decision (structured score), not generating content — there's no creative output to meter.

Availability

✅ Available now: Microsoft Foundry
⏳ Coming soon: OpenRouter

Why use a Qwen3.5 base?

Microsoft post-trained Decision-1 from Alibaba's open-weight Qwen3.5-9B model rather than training from scratch. This signals:

Open-weights maturity — Qwen3.5 series is competitive with closed models on classification and structured tasks
Lower training cost — Post-training a 9B model is dramatically cheaper than training a frontier model from scratch
Open ecosystem alignment — Microsoft's enterprise customers increasingly demand open-source compatibility for data sovereignty and fine-tuning

Post-training process (Microsoft has not disclosed full details):

Fine-tuned on classification and routing datasets
Optimized for calibrated probability output
Reinforcement learning for low decision-flip rate

Comparison: Decision-1 vs other recent "decision models"

Decision-1 isn't alone — October 2026 saw a wave of specialized decision/routing model launches:

Model	Vendor	Launch	Base	Pricing	Speed vs GPT-6
Decision-1	Microsoft	Oct 9	Qwen3.5-9B	$0.042/M in, free out	35× faster
Quyet-1.0-Large	Quyet AI	Oct 2026	Proprietary	TBD	7.7× faster
H2O-Lightning-4B v1.1	H2O.ai	Sept 2026	Mistral-4B	Free (open source)	14× faster
Liquid LFM-3-Router	Liquid AI	Aug 2026	Liquid LFM-3	Free (open source)	20× faster
Amazon Decision-Router	AWS	Aug 2026	Custom	$0.03/M in	25× faster
Databricks Route-9B	Databricks	Sept 2026	Custom	Enterprise pricing	30× faster

Pattern: Every major AI vendor now ships a specialized decision model. General-purpose LLMs are being unbundled into specialized components for production efficiency.

Industry context

Decision-1 launches into a market seeing massive shift toward production AI economics:

OpenAI, Anthropic, and Google all price frontier LLMs at $1-15/M input tokens
Specialized models can hit $0.04-0.10/M input tokens for narrow tasks
Routing 80% of classification to small models is the new industry default
Frontier models reserved for: creative writing, complex reasoning, multi-step planning

Microsoft's strategy: Ship specialized models for every enterprise workload, while reserving OpenAI/Anthropic API access for frontier tasks. Decision-1 sits alongside Copilot, Phi-4, and the MAI family.

Will Decision-1 replace GPT-6 for routing?

Not entirely — but for many tasks, yes.

Decision-1 wins when:

Task is structured classification (multi-choice)
Sub-second latency matters
High volume (>1M calls/month)
Cost sensitivity is high
Output is a decision, not content

GPT-6 still wins when:

Task requires complex reasoning
Open-ended generation is needed
Multi-step planning
Edge cases are common (decision-flip rate matters more)

Best practice: Use Decision-1 for routing + classification, GPT-6 for generation + reasoning. Microsoft's announcement explicitly frames Decision-1 as a complement, not a replacement.

Implications for AI economics

The Decision-1 launch is part of a larger trend — AI compute is being unbundled:

Workload	2024 cost	2026 cost	Reduction
Chat (frontier)	$5/M tokens	$3/M tokens	-40%
Classification (frontier)	$5/M tokens	$0.04/M tokens	-99%
Image generation (frontier)	$30/M tokens	$5/M tokens	-83%
Routing/decision (frontier)	$5/M tokens	$0.04/M tokens	-99%

Insight: Frontier models are increasingly being used only where they're needed, while specialized models handle 80%+ of production AI calls.

Caveats and unknowns

Vendor benchmarks only: 35× speed claim is from Microsoft, not third-party. Real-world performance may differ.
Narrow scope: Only handles structured classification, not open-ended tasks
Microsoft ecosystem lock-in: Decision-1 is built for Microsoft Foundry first; OpenRouter support is coming
No fine-tuning yet: Microsoft hasn't announced a fine-tuning API for Decision-1 (likely Q1 2027)
Qwen3.5 license compatibility: Decision-1 inherits Qwen3.5's Apache 2.0-derived license for the base model — fine for commercial use

FAQ: Microsoft Decision-1

What is Microsoft Decision-1?

A specialized AI model for routing, classification, and structured decision-making, post-trained from Alibaba's Qwen3.5-9B. Designed for high-volume, low-latency enterprise AI workflows.

How much does Decision-1 cost?

$0.042 per million input tokens with free output. At ~500 tokens per typical routing decision, that's ~$0.000021 per decision (less than 1/50th of a cent).

Is Decision-1 faster than GPT-6?

Yes — 35× faster on P50 latency for routing/classification workloads (Microsoft's internal benchmark). Output is structured JSON, not free-form text, which eliminates generation latency.

Can I use Decision-1 for chat or generation?

No — Decision-1 is a scoring model, not a chat model. For chat/generation, use GPT-6 Sol, Claude Haiku 5.5, or another chat model.

What is Decision-1 based on?

Qwen3.5-9B, an open-weight 9-billion-parameter language model from Alibaba. Microsoft post-trained it on classification and routing datasets.

Where can I use Decision-1?

Now: Microsoft Foundry. Coming soon: OpenRouter.

Is Decision-1 open source?

The base (Qwen3.5-9B) is open-source under a permissive license. Decision-1 itself is a Microsoft-proprietary post-training of Qwen3.5, so weights are not publicly released.

What is the context window?

32K tokens — sufficient for typical routing/classification prompts.

What is a "decision-flip rate"?

The percentage of times the model changes its decision when the input is perturbed (e.g., rephrasing). Microsoft reports 1.3% flip rate across 8 perturbation types — very low, indicating robust decisions.

Can I fine-tune Decision-1?

Not yet — Microsoft hasn't announced a fine-tuning API. Expect this in Q1 2027 based on Microsoft's historical patterns.

Bottom line: Microsoft Decision-1 is the latest signal that production AI is shifting from "one big model does everything" to "specialized models for each workload." For routing and classification at scale, Decision-1's 35× speed advantage and $0.042/M pricing make frontier models economically uneconomical for the same task.

For related coverage, see our Microsoft Surface Laptop Ultra launch, AMD MI400 vs NVIDIA Rubin, and OpenAI GPT-6 tier breakdown.