Model comparison
Microsoft-Decision-1 vs Jev 1.13
Compare Microsoft-Decision-1 and Jev 1.13 using the same provider-sourced ai decision models rubric. No mystery score and no invented benchmark ranking.
Model comparison
Compare Microsoft-Decision-1 and Jev 1.13 using the same provider-sourced ai decision models rubric. No mystery score and no invented benchmark ranking.
Quick take
Microsoft's decision model for sorting requests, choosing a route and scoring results without generating a chat response.
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.
TypeSafe AI's first System One model for fast, typed choices, scores, and probabilities that software can use directly.
Jev is intentionally literal and does not generate explanations or prose. TypeSafe documents weaknesses in arithmetic, counting, date comparison, indirection, noisy context, and adversarial instructions; keep deterministic logic in code and test every production decision boundary.
Compare the published facts
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 | Jev 1.13 |
|---|---|---|
| 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 | $0.042 / 1M input tokens; output free |
| Input capacityMaximum documented state and question capacity for one request. | 32,768 tokens | 64K tokens total; state plus longest question up to 32K |
| 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 | Early-access API and Playground; Python and JavaScript SDKs |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
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.
TypeSafe says it does not train or fine-tune on customer inputs. Its privacy policy does not promise a fixed zero-retention period and says services are hosted in the United States, so sensitive workloads should confirm contractual retention and processing terms.
Frequently asked questions
Microsoft-Decision-1: Microsoft's decision model for sorting requests, choosing a route and scoring results without generating a chat response. Jev 1.13: TypeSafe AI's first System One model for fast, typed choices, scores, and probabilities that software can use directly.
Consider Microsoft-Decision-1 when your priority is Support ticket classification and routing. Consider Jev 1.13 when your priority is High-volume classification and request routing. Test both with your own data and provider route before committing.
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.