Back to AI decision models
Model comparison
Microsoft-Decision-1 vs Clef
Compare Microsoft-Decision-1 and Clef 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
Cloudflare's larger decision model for classifying information and choosing an action without generating a chat response.
Best for
- Support ticket classification and routing
- Image-assisted document checks
- 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
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 |
|---|---|---|
| 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 | $0.24 / 1M input tokens |
| Input capacityMaximum documented state and question capacity for one request. | 32,768 tokens | 65,536 tokens; 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
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 FAQ
What is the main difference between Microsoft-Decision-1 and Clef?
Microsoft-Decision-1: Microsoft's decision model for sorting requests, choosing a route and scoring results without generating a chat response. Clef: Cloudflare's larger decision model for classifying information and choosing an action without generating a chat response.
Should I choose Microsoft-Decision-1 or Clef?
Consider Microsoft-Decision-1 when your priority is Support ticket classification and routing. Consider Clef 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 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.