Liquid d1 vs Microsoft-Decision-1

Liquid d1

The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.

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.

Liquid d1

1. POST to https://api.liquid.ai/decisions/v1/systemone with a liquid_ key as a Bearer header. This is not a chat-completions endpoint
2. Use d1 for images. d1:free is text-only and answers that it does not accept images
3. Send images as Base64 data URLs in images, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused
4. Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again
5. Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data

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