Model Releases
MiniMax M2.7: The Self-Evolving Coding Model Revolution
MiniMax releases M2.7, a 230B MoE model that writes its own code and beats GPT-5 benchmarks.
March 18, 2026
Model ReleaseMiniMax M2.7
Introduction
On March 18, 2026, MiniMax officially unveiled the M2.7, a groundbreaking coding model that represents a paradigm shift in the industry. This release marks a significant milestone in AI development, introducing the first self-evolving agent capable of participating in its own development cycle. Unlike traditional static models that require constant human retraining, M2.7 utilizes iterative self-assessment to refine its code generation capabilities without direct human intervention.
For developers seeking state-of-the-art performance, this open-source release represents a significant leap forward in autonomous software engineering. The model is designed to handle complex coding tasks that previously required human oversight, reducing the time-to-market for software projects significantly. By leveraging advanced agent teams, the model can collaborate on tasks that demand multiple perspectives, ensuring higher quality output in critical development environments.
- Release Date: 2026-03-18
- Category: Coding Model
- Open Source: Yes
Key Features & Architecture
The architecture behind MiniMax M2.7 is built on a massive 230-billion parameter Mixture-of-Experts system, with 10 billion parameters active during inference. This MoE structure allows the model to scale efficiently while maintaining high performance on specific coding tasks. It boasts a native 200,000 token context window, allowing it to ingest and reason over entire codebases in a single pass.
A standout feature is the Agent Teams capability, enabling native multi-agent collaboration where different specialized agents work together on complex tasks. The model is fully open-weighted on HuggingFace, ensuring transparency and community-driven improvement. This architecture supports 200K context, making it suitable for large-scale enterprise applications where context retention is critical for maintaining code integrity across vast repositories.
- Parameters: 230B MoE (10B active)
- Context Window: 200,000 tokens
- Architecture: Mixture-of-Experts
- Weights: Open on HuggingFace
Performance & Benchmarks
Performance metrics place MiniMax M2.7 at the forefront of the industry. On the SWE-Pro benchmark, the model achieves a score of 56.22%, matching the performance of GPT-5.3-Codex. In Terminal Bench 2, it scores 57.0%, securing the highest GDPval-AA ELO rating of 1495 among open-source models.
These numbers demonstrate that M2.7 rivals proprietary models like Claude Opus while maintaining a cost-effective structure. The self-evolving nature allows it to improve these scores over time without retraining from scratch. Developers can rely on these consistent high scores when integrating the model into CI/CD pipelines, knowing the output quality meets professional standards for software engineering tasks.