Model Releases
MiniMax M2: 230B Open-Source Model Released 2025
MiniMax M2 open-source 230B MoE model release details, pricing, benchmarks, and API access for developers.
October 23, 2025
Model ReleaseMiniMax M2
Introduction
MiniMax has officially unveiled the MiniMax M2, a groundbreaking open-source model released on October 23, 2025. This 230-billion-parameter Mixture-of-Experts architecture represents a significant leap forward for the community, offering weights that rival proprietary giants. Unlike closed ecosystems, MiniMax M2 prioritizes transparency and accessibility, allowing developers to fine-tune and deploy locally. This release marks a pivotal moment where enterprise-grade reasoning capabilities meet open weights, democratizing access to top-tier AI intelligence without the usual licensing restrictions. For engineers seeking high-performance inference without vendor lock-in, this is a critical development in the current AI landscape.
The model addresses the growing demand for transparent, high-capacity models that can be audited and modified. By releasing the weights, MiniMax ensures that the research community can validate claims regarding its self-evolving capabilities. This strategic move positions the company as a leader in open AI infrastructure, encouraging collaboration and innovation that closed-source models cannot facilitate. The M2 release signifies a shift towards a more collaborative future for artificial intelligence development.
- Release Date: October 23, 2025
- Status: Open Weights Available
- Architecture: 230B MoE
Key Features & Architecture
The architecture utilizes a sophisticated MoE design, activating only specific experts per token to optimize inference speed and reduce memory footprint. It supports a massive context window, enabling long-form document analysis and complex multi-step reasoning tasks within a single session. Multimodal capabilities are integrated natively, allowing for seamless text-to-image understanding and visual grounding. Key specifications include a 256k token context window and support for various programming languages including Python, C++, and JavaScript. The model is trained on a diverse dataset, ensuring robust performance across different domains.
Efficiency is paramount in the M2 design, allowing for faster training times compared to dense models of similar size. The mixture-of-experts structure dynamically routes queries to the most relevant sub-networks, improving accuracy without linearly increasing compute costs. This architectural choice is particularly beneficial for agentic workflows where the model must process diverse information streams efficiently. Furthermore, the open nature of the weights invites third-party optimizations and quantization efforts from the community, further enhancing accessibility for resource-constrained environments.