MiniMaxAI/MiniMax-M2.5
text-generation model by MiniMaxAI
From the publisher
Model card & documentation
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Join Our community. Today we're introducing our latest model, MiniMax-M2.5. Extensively trained with reinforcement learning in hundreds of thousands of complex real-world environments, M2.5 is SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks, boasting scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp (with context management). Trained to reason efficiently and decompose tasks optimally, M2.5 exhibits tremendous speed in performing complicated agentic tasks, completing the SWE-Bench Verified evaluation 37% faster than M2.1, matching the speed of Claude Opus 4.6. M2.5 is the first frontier model where users do not need to worry about cost, delivering on the promise of intelligence too cheap to meter. It costs just $1 to run the model continuously for an hour at a rate of 100 tokens per second. At 50 tokens per second, the cost drops to $0.30. We hope…
Read the full model card ↗ · Preview checked 2026-10-31T14:26:20.905Z
Inside the original model card — Document outline
- Coding
- Search and Tool calling
- Office work
- Efficiency
- Cost
- Improvement Rate
- RL Scaling
- Forge –– Agent-Native RL Framework
- Agentic RL Algorithm and Reward Design
- MiniMax Code: M2.5 as a Professional Employee
- How to Use
- Local Deployment Guide
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Documentation belongs to its respective authors. Reported project/model license: other. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
MiniMaxAI/MiniMax-M2.5 is a text-generation model repository published by MiniMaxAI on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- transformers
- Recent downloads (30 days)
- 1,405,083
- Cumulative likes
- 1,529
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 228,689,764,864
- Architecture
- MiniMaxM2ForCausalLM
- License
- other
- Access
- Not gated by Hugging Face
- Created
- 2026-02-12T06:05:24.000Z
- Last modified
- 2026-10-26T07:08:12.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:29.042Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.