Description
MiniMax-M2.5 is the latest model from MiniMaxAI, aimed at advancing and democratizing artificial intelligence through open source and open science. This model has been extensively trained using reinforcement learning in hundreds of thousands of complex real-world environments, achieving state-of-the-art (SOTA) performance in coding, agentic tool use, search, and office work. With impressive scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp, MiniMax-M2.5 demonstrates its capability in economically valuable tasks.
One of the standout features of MiniMax-M2.5 is its speed and efficiency. It completes the SWE-Bench Verified evaluation 37% faster than its predecessor, M2.1, matching the speed of Claude Opus 4.6. The model is designed to reason efficiently and decompose tasks optimally, making it suitable for a wide range of applications. Users can run the model continuously for an hour at a cost of just $1 at a rate of 100 tokens per second, or $0.30 at 50 tokens per second, making it a cost-effective solution for various tasks.
In programming evaluations, MiniMax-M2.5 has shown substantial improvements, reaching SOTA levels in multilingual tasks. The model's ability to think and plan like an architect allows it to decompose and plan features, structure, and UI design effectively. Trained on over 10 programming languages, it supports the entire development lifecycle, from system design to comprehensive code review.
MiniMax-M2.5 also excels in search and tool calling, achieving industry-leading performance in benchmarks such as BrowseComp and Wide Search. Its improved decision-making capabilities allow it to handle complex tasks more efficiently, using fewer search rounds and demonstrating better token efficiency.
In office scenarios, MiniMax-M2.5 has been trained to produce deliverable outputs, collaborating with professionals in finance, law, and social sciences to enhance its capabilities. The model has shown significant improvements in high-value workspace scenarios, achieving an average win rate of 59.0% against other mainstream models. Overall, MiniMax-M2.5 represents a significant advancement in AI technology, providing users with powerful tools for coding, office work, and beyond.
MiniMax-M2.5 Highlights
State-of-the-art performance in coding
Efficient task decomposition
Cost-effective operation
Supports over 10 programming languages
Industry-leading search capabilities
High-value office work outputs
Reinforcement learning trained
Fast processing speed
Getting Started with MiniMax-M2.5
Access model: Visit the Hugging Face repository.
Authenticate: Set up your credentials for API access.
Set up environment: Prepare your development environment for integration.
Integrate via API: Use the provided API to connect with MiniMax-M2.5.
Optimize: Adjust parameters for best performance based on your use case.
MiniMax-M2.5's Use Cases
- Software Development
- Office Automation
- Search Optimization
- Financial Modeling
- Multilingual Programming
