Model Releases
MiniMax M2.1: The 230B Open Source Coding Model That Disrupts Pricing
MiniMax releases M2.1, a 230B MoE coding model offering SWE-bench scores of 74% at 92% lower cost than Western competitors.
December 1, 2025
Model ReleaseMiniMax M2.1
Introduction
On December 1, 2025, MiniMax unveiled the M2.1 model, marking a significant milestone in the open-source coding landscape. This release is not merely an incremental update but a paradigm shift designed to bring multilingual programming gains to open AI models. As developers seek high-performance coding assistants without the prohibitive costs associated with proprietary giants, M2.1 steps forward as a fully open-source SOTA coding model. It addresses the critical need for accessibility in enterprise automation while maintaining the rigorous standards required for complex software engineering tasks.
The model is positioned to compete directly with Western alternatives, offering a compelling value proposition for startups and large enterprises alike. By focusing on real-world productivity and everyday office automation, MiniMax has demonstrated that open weights can deliver intelligence too cheap to meter. This announcement follows a frenetic week of releases from domestic rivals, yet M2.1 distinguishes itself through its architectural efficiency and benchmark performance.
- Released: December 1, 2025
- Category: Coding Model
- Status: Fully Open Source
Key Features & Architecture
The M2.1 architecture is built upon a massive Mixture-of-Experts (MoE) foundation, totaling 230 billion parameters. However, its efficiency is derived from a sparse activation strategy where only 10 billion parameters are active per token. This design choice significantly reduces inference latency and computational overhead without sacrificing the model's raw knowledge base. The model supports a context window optimized for long-form codebases, allowing it to understand entire repositories within a single pass.
Beyond standard coding tasks, M2.1 includes enhanced multimodal capabilities and multilingual support, making it versatile for global development teams. The model is fine-tuned specifically for agentic capabilities, enabling it to perform autonomous tasks such as debugging, refactoring, and deployment pipelines. This focus on practical utility ensures that the model is not just a chat interface but a functional tool for software engineering workflows.
- Total Parameters: 230B MoE
- Active Parameters: 10B per token
- Architecture: Mixture-of-Experts
- Support: Multilingual & Multimodal
Performance & Benchmarks
In terms of raw capability, M2.1 achieves a SWE-bench score of 74.0%, placing it at the state-of-the-art level for open-source coding models. This metric is crucial for developers as it measures the model's ability to solve real-world GitHub issues. Furthermore, the model demonstrates exceptional performance on HumanEval, often rivaling closed-source models in specific coding domains. Its reasoning capabilities have been significantly enhanced over previous versions, allowing it to handle complex logical deductions within code.