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Model Drop

MAI-Code-1-Flash

MAI-Code-1-Flash
1 min read
MAI-Code-1-Flash

MAI-Code-1-Flash, which Microsoft AI shipped on June 2, 2026, is a small in-house coding model trained inside GitHub Copilot's own harness and rolled out first to Copilot users in VS Code.

  • Model: MAI-Code-1-Flash in GitHub Copilot

  • Maker: Microsoft

  • Ship date: June 2, 2026

  • Pricing: GitHub Copilot only, billed at $0.75 / $4.50 per M tokens

  • Context: 256K tokens

  • Where you can use it:

    • GitHub Copilot in VS Code, picker and Auto

    • More Copilot apps and plans later in June

  • When to reach for it:

    • Quick edits, refactors and repo Q&A

    • Cheap first pass in Copilot's Auto mode

    • Skip it for tricky logic or design work

  • How it compares:

    • Pitched straight at Claude Haiku 4.5

    • Small-tier peer of Gemini 3.5 Flash

  • What makes it different:

    • Trained in Copilot's production harness

    • Spends fewer tokens on easy requests

  • Early reactions: Hacker News readers called the launch thin on technical detail and doubted it could compete with Claude, GPT or Gemini, while later hands-on reviews found it fast and cheap but shaky on harder tasks.

  • Read more: Introducing MAI-Code-1-Flash, model card

The benchmarks

Microsoft ran every coding row in the same VS Code-based GitHub Copilot production harness for both models, and its only comparison model is Claude Haiku 4.5, so these are internal evals against a single small rival.

Microsoft's numbers

  • SWE-bench Pro (harder real-repo fixes): 51.2%. +16.0 over Claude Haiku 4.5 (35.2%).

  • SWE-bench Verified (real GitHub fixes): 71.6%. +5.0 over Claude Haiku 4.5 (66.6%).

  • Terminal-Bench 2.0 (agentic terminal tasks): 54.8%. +13.2 over Claude Haiku 4.5 (41.6%).

  • SWE-Bench Multilingual (fixes across languages): 65.5%. +2.8 over Claude Haiku 4.5 (62.7%).

  • τ²-bench Telecom (agentic tool use): 71.7%. +17.0 over Claude Haiku 4.5 (54.7%).

  • GPQA Diamond (grad-level science): 84.6%. +11.4 over Claude Haiku 4.5 (73.2%).

  • AIME 2026 (competition math): 92.5%. +9.2 over Claude Haiku 4.5 (83.3%).

  • Artifacts Bench (visual coding): 36.4%. −0.2 under Claude Haiku 4.5 (36.6%).

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