Intelligence index
Outperforms 56% of compared models
MiniMax M2.1
MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world capability while maintaining exceptional latency, scalability, and cost efficiency.
Compared to its predecessor, M2.1 delivers cleaner, more concise outputs and faster perceived response times. It shows leading multilingual coding performance across major systems and application languages, achieving 49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual, and serves as a versatile agent “brain” for IDEs, coding tools, and general-purpose assistance.
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
- $0.3/$1.2per 1M tokens
- Context
- 205K
- Released
- 2025-12-23
Pricing
Artificial Analysis
Explore rankingsDesign Arena
Elo scores and win rates across design and agent tasks.
Category performance
1,000-1,500 EloModel Arena
UI Component
1218Elo
Win rate 60.9%
Data Visualization
1206Elo
Win rate 57%
SVG
1128Elo
Win rate 55.4%
3D
1162Elo
Win rate 57.5%
Code Categories
1188Elo
Win rate 55.3%
Website
1201Elo
Win rate 55.4%
Game Development
1134Elo
Win rate 50.4%
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
- 1.0M
- Max output
- 524.3K
- Released
- 2026-10-19
- Input
- $0.15/M
- Output
- $0.6/M
- Cache read
- $0.015/M
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
- 512K
- Released
- 2026-05-31
- Input
- $0.3/M
- Output
- $1.2/M
- 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