---
title: "MAI-Thinking-1"
url: "https://handyai.news/modeldrop/mai-thinking-1"
published: "2026-08-12T17:00:00.000Z"
section: "Model Drop"
description: "MAI-Thinking-1, which Microsoft AI announced at Build on June 2, 2026 and opened to the public as a Foundry preview on August 12, 2026, is Microsoft's first…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"MAI-Thinking-1,\" August 12, 2026, https://handyai.news/modeldrop/mai-thinking-1."
comments: "https://handyai.news/api/article/79624/comments"
---

# MAI-Thinking-1

*Published August 12, 2026 · Model Drop*

MAI-Thinking-1, which Microsoft AI announced at Build on June 2, 2026 and opened to the public as a Foundry preview on August 12, 2026, is Microsoft's first in-house reasoning model, a mid-sized mixture-of-experts trained from scratch without distillation.

- Model: MAI-Thinking-1 (`MAI-Thinking-1`)
- Maker: Microsoft
- Ship date: August 12, 2026 (public preview; private preview from June 2)
- Pricing: $2 / $8 per M tokens
- Context: 256K tokens
- Where you can use it: Microsoft Foundry public preview
- MAI Playground

**When to reach for it**:

- Math and quantitative reasoning on Azure
- Enterprises that need clean data lineage
- Skip it for terminal-heavy agent work

**How it compares**:

- Mid-tier rival to Claude Sonnet 5, Kimi K3
- Microsoft only charted spring-era rivals

**What makes it different**:

- Trained from scratch, no distillation
- Microsoft's first in-house reasoning model

**Early reactions**: Early write-ups called it a credible efficiency result for its size, but noted it doesn't lead the field and Microsoft's claims were unverified.

**Read more**: [Introducing MAI-Thinking-1](https://microsoft.ai/news/introducing-mai-thinking-1/), [technical report](https://microsoft.ai/wp-content/uploads/2026/06/main_20260602_2.pdf), [Foundry model card](https://ai.azure.com/catalog/models/MAI-Thinking-1)

## The benchmarks

Microsoft's scores average 4 runs at temperature 1 with up to 256K tokens; rival scores come from each lab's own model card, and by the August public preview most rivals Microsoft charted had newer successors (Claude Opus 4.8, Claude Sonnet 5, Kimi K3). The MMLU-Pro row compares against Microsoft's own run of Claude Sonnet 4.6 at max effort.

#### Microsoft's numbers

- SWE-bench Pro (harder real-repo fixes): 52.8%. −0.6 under Claude Opus 4.6 (53.4%), −4.9 under GPT-5.4 (57.7%), −5.8 under Kimi K2.6 (58.6%).
- SWE-bench Verified (real GitHub fixes): 73.5%. +0.4 over DeepSeek V3.2 (73.1%), −6.1 under Claude Sonnet 4.6 (79.6%), −7.3 under Claude Opus 4.6 (80.8%).
- Terminal-Bench 2.0 (agentic terminal tasks): 46.0%. −0.4 under DeepSeek V3.2 (46.4%), −13.1 under Claude Sonnet 4.6 (59.1%), −29.1 under GPT-5.4 (75.1%).
- AIME 2025 (competition math): 97.0%. +1.4 over Claude Sonnet 4.6 (95.6%), +3.9 over DeepSeek V3.2 (93.1%), −2.8 under Claude Opus 4.6 (99.8%).
- GPQA Diamond (grad-level science): 84.2%. +1.8 over DeepSeek V3.2 (82.4%), −5.7 under Claude Sonnet 4.6 (89.9%), −8.6 under GPT-5.4 (92.8%).
- LiveCodeBench (v6, competitive coding): 87.7%. +4.4 over DeepSeek V3.2 (83.3%), −1.9 under Kimi K2.6 (89.6%), −5.8 under DeepSeek V4 (93.5%).
- MMLU-Pro (broad knowledge): 85%. −2 under Claude Sonnet 4.6 (87%).


---

Source: https://handyai.news/modeldrop/mai-thinking-1

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