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Model comparison
Microsoft-Decision-1 vs Clef-flash
Compare Microsoft-Decision-1 and Clef-flash using the same provider-sourced ai decision models rubric. No mystery score and no invented benchmark ranking.
Quick take
Microsoft-Decision-1
Microsoft's decision model for sorting requests, choosing a route and scoring results without generating a chat response.
Best for
- Support ticket classification and routing
- Grading AI responses against a rubric
- Agent decisions with human escalation
Watch out for
Text-only: no image, audio or video inputs, and no written rationale. Test wording and probability thresholds. Do not use its scores as the sole basis for consequential decisions about people.
Clef-flash
A smaller Cloudflare decision model for frequent routing and classification tasks, with a lower input-token price than Clef.
Best for
- Support ticket classification and routing
- High-volume structured decisions
- Agent decisions with human escalation
Watch out for
This is not a chatbot and does not explain its decisions in prose. Test probability thresholds on your own examples. Hosted requests can truncate long state, and self-hosted limits depend on configuration.
Compare the published facts
Microsoft-Decision-1 vs Clef-flash
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| AI decision models | Microsoft-Decision-1 | Clef-flash |
|---|---|---|
| Input priceCurrent provider price for the text or structured state submitted to the model, including whether decision outputs are billed. | $0.042 / 1M input tokens; output free | $0.038 / 1M input tokens |
| Input capacityMaximum documented state and question capacity for one request. | 32,768 tokens | 24,576 tokens on Workers AI; up to 64 questions per request |
| Decision typesThe bounded question forms the model can answer, such as choices, scores, or probabilities. | Choice, Score, and Noul probability questions | Choice, Score, and Noul probability questions |
| Output contractHow the response is constrained for direct use by application code. | Typed JSON decisions defined before inference | Typed JSON decisions defined before inference |
| UncertaintyWhether the endpoint exposes probabilities, confidence, or another calibrated signal. | Probabilities for each option; validate thresholds on your own data | Probability distributions; Choice and Score also include confidence |
| Where to use itCurrent API, SDK, playground, and access-stage information. | Microsoft Foundry and OpenRouter | Workers AI API and self-hosted weights |
How to choose
Compare the job, not the hype.
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
Microsoft-Decision-1
Microsoft says Foundry inputs and outputs are not used to train foundation models without permission. Abuse monitoring can retain flagged content for review. Confirm retention and residency for your deployment; OpenRouter adds its own data-handling terms.
Clef-flash
Cloudflare says Workers AI customer content is not used for training or service improvement without explicit consent. Connected storage services can retain content. Self-hosted deployments follow your own storage and access controls.
Provider and API links
Frequently asked questions
Microsoft-Decision-1 vs Clef-flash FAQ
What is the main difference between Microsoft-Decision-1 and Clef-flash?
Microsoft-Decision-1: Microsoft's decision model for sorting requests, choosing a route and scoring results without generating a chat response. Clef-flash: A smaller Cloudflare decision model for frequent routing and classification tasks, with a lower input-token price than Clef.
Should I choose Microsoft-Decision-1 or Clef-flash?
Consider Microsoft-Decision-1 when your priority is Support ticket classification and routing. Consider Clef-flash when your priority is Support ticket classification and routing. Test both with your own data and provider route before committing.
Is this Microsoft-Decision-1 vs Clef-flash comparison based on Cody benchmarks?
No. This comparison aligns provider-published facts for the AI decision models category. It does not claim a universal winner or combine incompatible third-party benchmark scores.