# Microsoft-Decision-1 vs ScaleDown

> ScaleDown scores 48.6 (D) to Microsoft-Decision-1's 38.8 (E) for decision models. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-10-15

ScaleDown scores 48.6 (D) on agent readiness against Microsoft-Decision-1's 38.8 (E), and leads in 5 of 7 scored categories. Microsoft-Decision-1 leads on reliability. Both do decision models.

- Microsoft-Decision-1: grade E, 38.8/100, rank #1008 of 1094. Markdown https://www.anchorterminal.com/tools/microsoft-decision-1.md · JSON https://www.anchorterminal.com/api/v1/tools/microsoft-decision-1.json
- ScaleDown: grade D, 48.6/100, rank #880 of 1094. Markdown https://www.anchorterminal.com/tools/scaledown.md · JSON https://www.anchorterminal.com/api/v1/tools/scaledown.json
- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md
- All 136 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md
- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md
- All 125 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md

## Which one, for what

### Microsoft-Decision-1 (E)

Good for: Routing, classification, prioritisation and rubric checks over text, where a fixed set of options and a low price per token matter more than generated text.

Ahead on:
- Reliability, 30 against 20

Watch for: Public preview only. Microsoft's post gives no general availability date and states no licence for the hosted model

### ScaleDown (D)

Good for: Cheap, high-volume labelling, routing and yes or no checks where a probability is more useful than prose, and for pulling described fields with offsets from text, images or PDFs.

Ahead on:
- Schema & documentation, 69 against 43
- Agent ergonomics, 76 against 50
- Payments & pricing, 40 against 20
- Maintenance & community, 56 against 40
- Transparency & trust, 54 against 29

Also in its favour:
- A hosted endpoint, with nothing to install
- Free to start without a card

Watch for: No status page, no published rate limits and no SLA outside enterprise contracts


## Score by category

| Category | Weight | Microsoft-Decision-1 | ScaleDown | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 30 | 20 | Microsoft-Decision-1 +10 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 43 | 69 | ScaleDown +26 |
| Agent ergonomics | 13% (16.2 this run) | 50 | 76 | ScaleDown +26 |
| Security & auth | 14% (17.5 this run) | 52 | 48 | Microsoft-Decision-1 +4 |
| Payments & pricing | 10% (12.5 this run) | 20 | 40 | ScaleDown +20 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 40 | 56 | ScaleDown +16 |
| Transparency & trust | 7% (8.8 this run) | 29 | 54 | ScaleDown +25 |
| Negative events | ≤15 | 0 | -2 | |
| **Total** | | **38.8 · E** | **48.6 · D** | |

## Facts side by side

| Fact | Microsoft-Decision-1 | ScaleDown |
| --- | --- | --- |
| Kind | Model API | Model API |
| Vendor | Microsoft | Carbon ScaleDown, Inc. |
| Hosted endpoint | no (local only) | `https://api.scaledown.xyz` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| Price for decision models | free | not published |
| x402 | no | no |
| Licence | Not stated. The announcement and OpenRouter's model page name no licence, and no weights repository was found. | Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-09 | 2026-10-14 |
| Terms last updated | no document linked | couldn't be read |
| Privacy policy last updated | no date given | couldn't be read |
| Customer content may train models |  | couldn't be read |
| Terms restrict automated access |  | couldn't be read |
| Terms restrict benchmarking |  | couldn't be read |
| Terms or service can change without notice |  | couldn't be read |
| Arbitration or class-action waiver |  | couldn't be read |
| Popularity | none | 0 stars |

## Verdicts

**Microsoft-Decision-1.** Microsoft publishes $0.042 per million input tokens, with output free, and the model is callable through Microsoft Foundry and OpenRouter. It is in public preview. No licence, model-specific retention statement, rate limit or deprecation policy was found, and the Foundry route needs an Azure deployment and an Entra token. Latency and accuracy figures are Microsoft's claims.

**ScaleDown.** Classify answers Jev-shaped `choice`, `noul` and `score` questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other.

## Before you call either

### Microsoft-Decision-1

1. Use OpenRouter's Decisions method, not an OpenAI chat-completions SDK. OpenRouter says chat completions SDKs will not work with this model
2. On Foundry, send the request to the deployment's /providers/microsoft/v1/systemone path with a Microsoft Entra token for https://cognitiveservices.azure.com/.default, not an API key
3. Take the deployment name from the Foundry quickstart before the first call. Microsoft says to confirm the route and authentication header, and the pages reviewed do not give the name
4. Keep each request within OpenRouter's 32,768-token context and send only fixed options, since the model is not intended for open-ended generation
5. Measure latency and calibration on your own labelled cases before relying on Microsoft's latency and 'nine times out of 10' statements

### ScaleDown

1. Send several questions about one item in one `/v1/scaledown` call. Each becomes its own Classify call and the usage is summed
2. Pin `classify-1`, `extract-1` or `summarize-1` once you've tuned thresholds on your own labelled cases
3. Set `context_chars` to 0 and use `top_n` on Extract unless you need the 500 characters of context each side
4. Set `reasoning: false` on /classify when you only need the label. The reasoning call bills its input at 2x
5. Back off and retry on 429, 502 and 504. No limits are published and calls have no side effects

## Questions

### Which is better for AI agents, Microsoft-Decision-1 or ScaleDown?

ScaleDown scores 48.6 (D) on agent readiness against Microsoft-Decision-1's 38.8 (E), and leads in 5 of 7 scored categories. Microsoft-Decision-1 leads on reliability.

### Do Microsoft-Decision-1 and ScaleDown need an API key?

Microsoft-Decision-1 takes an API key or an OAuth sign-in. ScaleDown needs an API key.

### Can an agent call Microsoft-Decision-1 and ScaleDown without installing anything?

No hosted endpoint is listed for Microsoft-Decision-1. ScaleDown has a hosted endpoint at https://api.scaledown.xyz.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/microsoft-decision-1-vs-scaledown.json, and with the fewest tokens: https://www.anchorterminal.com/compare/microsoft-decision-1-vs-scaledown.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "microsoft-decision-1", "b": "scaledown"}`. From a terminal: `anchor compare microsoft-decision-1 scaledown`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/microsoft-decision-1.json and https://www.anchorterminal.com/api/v1/tools/scaledown.json

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- [Microsoft-Decision-1 vs Vela 2.0](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-vela.md)
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