MiniMax-M2: Advanced AI for Coding and Agent Workflows
MiniMax-M2 is a powerful 230B parameter MoE (Mixture of Experts) AI model designed specifically for coding and intelligent agent workflows. With its massive 204K context window and exceptional programming capabilities, it delivers enterprise-grade performance while maintaining cost efficiency. Released under Apache 2.0 license, it's completely open-source and ready for commercial use.
What is MiniMax-M2
MiniMax-M2 is a breakthrough 230 billion parameter AI model built with Mixture of Experts (MoE) architecture, activating only 10 billion parameters at a time for maximum efficiency. This innovative design delivers exceptional performance in coding, agent workflows, and general intelligence tasks while using significantly fewer computational resources than traditional models. With an unprecedented 204K token context window and 131K maximum output capacity, it can handle complex multi-file projects and long-form code generation with ease. Released under the Apache 2.0 license, MiniMax-M2 is completely open-source and commercially friendly, making advanced AI accessible to developers and businesses worldwide.
- Advanced Programming IntelligenceBuilt specifically for developers, MiniMax-M2 excels at code generation, multi-file editing, debugging workflows, and test-driven development with industry-leading accuracy.
- Massive Context UnderstandingWith 204K token context window, it can process entire codebases, long documents, and complex project structures while maintaining coherent understanding throughout.
- Cost-Effective ArchitectureMoE design activates only 10B of 230B parameters per task, delivering superior performance at just 8% the cost of comparable models.
Key Features of MiniMax-M2
Discover the powerful capabilities that make MiniMax-M2 the ideal choice for modern development workflows.
Mixture of Experts Architecture
Advanced MoE design with 230B total parameters and 10B active parameters, delivering maximum performance with minimal computational overhead for cost-effective AI solutions.
Ultra-Large Context Window
Industry-leading 204K token context window allows processing of entire codebases, complex documentation, and multi-file projects without losing important context.
Superior Coding Capabilities
Optimized for programming tasks including code generation, multi-file editing, compile-run-fix loops, debugging, and test validation with exceptional accuracy.
Intelligent Agent Workflows
Designed for complex agentic tasks with tool integration, seamless workflow automation, and the ability to handle multi-step problem-solving processes.
Open Source Freedom
Released under Apache 2.0 license, providing complete freedom for commercial use, modification, and distribution without licensing restrictions or fees.
Exceptional Performance Efficiency
Ranks #1 among global open-source models while using only 8% of the computational cost compared to similar-sized traditional models.
What People Are Talking About MiniMax-M2 on X
Join the conversation about MiniMax-M2 and share your experience with the developer community
We’re open-sourcing MiniMax M2 — Agent & Code Native, at 8% Claude Sonnet price, ~2x faster
— MiniMax (official) (@MiniMax__AI) October 27, 2025
⚡ Global FREE for a limited time via MiniMax Agent & API
- Advanced Coding Capability: Engineered for end-to-end developer workflows. Strong capability on a wide-range of applications… pic.twitter.com/FoiAz9NF4q
MiniMax’s M2 achieves a new all-time-high Intelligence Index score for an open weights model and offers impressive efficiency with only 10B active parameters (200B total)
— Artificial Analysis (@ArtificialAnlys) October 27, 2025
Key takeaways:
➤ Efficiency to serve at scale: MiniMax-M2 has 200B total parameters and is very sparse with… pic.twitter.com/Z2ktn6s83z
💡Some fun facts about Minimax M2:
— Yifan Zhang (@yifan_zhang_) October 27, 2025
1. Minimax uses GPT-OSS-like structure, i.e., Full Attention interleaved with Sliding Window Attention (SWA).
2. It uses QK Norm, and every single attention head has its own unique, learnable RMSNorm.
3. The full attention and SWA parts… https://t.co/XBsvPFhBVt pic.twitter.com/CoSzljm3NB
💡Some fun facts about Minimax M2:
— Yifan Zhang (@yifan_zhang_) October 27, 2025
1. Minimax uses GPT-OSS-like structure, i.e., Full Attention interleaved with Sliding Window Attention (SWA).
2. It uses QK Norm, and every single attention head has its own unique, learnable RMSNorm.
3. The full attention and SWA parts… https://t.co/XBsvPFhBVt pic.twitter.com/CoSzljm3NB
MiniMax M2 + Claude Code + Skills 💪🏻 pic.twitter.com/xAbvDYnIAm
— Ivan Fioravanti ᯅ (@ivanfioravanti) October 26, 2025
MiniMax-M2 vs Qwen3 235B A22B
— Maziyar PANAHI (@MaziyarPanahi) October 27, 2025
> Input: Complex clinical case of paraneoplastic SIADH and Lambert–Eaton myasthenic syndrome revealing small-cell lung carcinoma.
> MiniMax-M2 hosted locally on 4xA100 (FP8)
> Qwen3 235B A22B on OpenRouter pic.twitter.com/D4pcfKWxFI
Live in Cline: @MiniMax__AI M2
— Cline (@cline) October 27, 2025
Temporarily free (i.e. money = $0) in Cline! https://t.co/q0Nc6imyL5 pic.twitter.com/UxUULgNlMY
MiniMax-M2 by @MiniMax__AI now on MLX 🚀
— Prince Canuma (@Prince_Canuma) October 27, 2025
Here are the results on M3 Ultra
PR and model weights 👇🏽 pic.twitter.com/pMDQc11OMV
🚨 MiniMax just open-sourced MiniMax-M2.
— Alvaro Cintas (@dr_cintas) October 27, 2025
This model is built for advanced coding and agentic workflows with strong general intelligence.
- 8% of Sonnet’s cost
- 2x faster inference
- 230B params (10B active)
- Works with Claude Code, Cursor & more
Free via MiniMax Agent & API. pic.twitter.com/BBZmqZPd9d
