Intelligence index
Outperforms 51% of compared models
MiniMax M2
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.
The model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors.
Benchmarked by Artificial Analysis, MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency.
To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our docs.
- Input / output price
- $0.255/$1.02per 1M tokens
- Context
- 205K
- Released
- 2025-10-23
Pricing
Artificial Analysis
Explore rankingsDesign Arena
Elo scores and win rates across design and agent tasks.
Category performance
1,000-1,500 EloModel Arena
SVG
1098Elo
Win rate 55.3%
Data Visualization
1140Elo
Win rate 50%
UI Component
1131Elo
Win rate 49.2%
3D
1095Elo
Win rate 48.3%
Game Development
1118Elo
Win rate 48.1%
Code Categories
1133Elo
Win rate 48.1%
Website
1141Elo
Win rate 48%
Related Models
MiniMax M3.1 Flash
minimaxMiniMax-M3.1-Flash is a multimodal model from MiniMax. Compared with M3, it delivers significant improvements across multiple benchmarks, particularly in complex agentic tasks, coding, and cybersecurity. It is built for complex, multi-step co-work automation, understanding real codebases, fixing bugs, and building features, and visual understanding and creation, including frontend and presentation creation. It is an open-weight mixture-of-experts model with 23B active / 428B total parameters.
- Context
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- Max output
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- Released
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- Input
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- Output
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- Cache read
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MiniMax M3
minimaxMiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding, and tool use. It is built on MiniMax Sparse Attention (MSA), which replaces full attention with KV-block selection to cut per-token compute at long context — roughly 1/20 the cost of the previous generation at 1M tokens, with substantially faster prefill and decode while retaining quality across most tasks. Trained as a native multimodal model on interleaved data and tuned for multi-turn, production-like collaboration via an interactive user-simulator framework, the model is oriented toward sustained, multi-step tasks rather than single-turn execution.
- Context
- 1.0M
- Max output
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- Released
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- Input
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- Output
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- Cache read
- $0.06/M
MiniMax M2.5
minimaxMiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams. Scoring 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp, M2.5 is also more token efficient than previous generations, having been trained to optimize its actions and output through planning.
- Context
- 205K
- Max output
- 131.1K
- Released
- 2026-02-12
- Input
- $0.3/M
- Output
- $1.2/M
- Cache read
- $0.03/M