MAI-DS-R1 by Azure Cognitive Services

$10 off Synthetic - fastest OpenCode provider$10 off Synthetic - the fastest Kimi K2.6 and GLM 5.1 provider Discuss

Model details

MAI-DS-R1

Azure Cognitive Servicesmai-ds-r1mai

Open provider page Provider docs

Quick Info

Provider: Azure Cognitive Services
Model key: mai-ds-r1
Release date: Jan 20, 2025
Last updated: Jan 20, 2025

Cost

Input token cost: $1.35
Output token cost: $5.40

Limits

Output tokens: 8,192 tokens
Context window

Latest news about MAI-DS-R1

No articles yet. Fetch the latest news to show it here.

Videos about MAI-DS-R1

Recent tweets and retweets from Azure Cognitive Services

Jun 2, 2026, 10:00 PMUTC

In a world where every GPU matters, Managed Compute in Foundry Models gives you global reach with zero infrastructure overhead by routing your workloads to available capacity automatically and globally. msft.it/6014vjJno

Jun 2, 2026, 7:15 PMUTC

x.com/i/article/206160520564…

Jun 2, 2026, 6:34 PMUTC

An open, end-to-end trust stack for AI agents on any framework: run ASSERT to find where your agent fails policy, use ACS to place the right controls at the right checkpoints, then re-run ASSERT to confirm. Evaluation → enforcement, closed loop: msft.it/6011vj6g9

Jun 2, 2026, 5:53 PMUTC

Stop stitching vendors together to run open models in production. Fireworks AI on Microsoft Foundry is GA. One endpoint. Enterprise SLAs. Zero setup. Build your next production workload with Fireworks AI on Foundry: msft.it/6018vbCUI Video

Jun 2, 2026, 5:36 PMUTC

Stop building RAG pipelines from scratch. Foundry IQ is the agent-native knowledge layer behind your Foundry agents. Every source in one knowledge base - Work IQ, Fabric IQ, and now Web IQ. Serverless grounding, zero friction. Available today. msft.it/6012vb7La Video

Recent tweets and retweets from Azure Cognitive Services

More models around MAI-DS-R1

Browse family

Also served by

This exact model name is also listed by 2 other providers.

Azure GitHub Models

Discuss this model

Powered byHyvor Talk

Add corrections, implementation notes, pricing changes, or usage caveats for other readers.