Model reference · open weights

MiniMax-M2.1

LLMs MiniMaxAI Text gen 1 build Its own licence terms 31k dl/mo

MiniMax-M2.1 is an open-weight language model from MiniMax. MiniMax-M2.1 (FP8) weighs 230 GB; the smallest configuration that runs it is 2× H200 141 GB.

  • MiniMax-M2.1 is a 228.7B parameter text-generation model developed by MiniMax and released under a Modified-MIT license.
  • It is designed for agentic tasks, including coding, tool use, instruction following, and long-horizon planning.
  • The model supports local deployment via frameworks such as SGLang, vLLM, and Transformers, with recommended inference parameters of temperature 1.0, top_p 0.95, and top_k 40.

Summary of the MiniMaxAI/MiniMax-M2.1 model card, 2026-10-01

What it is

Released byMiniMax
Released2025-12-20
Parameters228.7B
VRAM230 GB for the weights

What it runs on

Memory and cards for MiniMax-M2.1 (FP8)

230 GBweights, file size
254 MBcache per 1K tokens
697 MBruntime overhead, at least
196,608 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … B200 180 GB
12 smaller cards
———
2× H200 141 GB
tensor parallel
164135K138 GB a card
4× H100 80 GB
tensor parallel
297all 192K78.1 GB a card
4× A100 80 GB
tensor parallel · FP8 without its speed-up here
389all 192K78.2 GB a card
2× B200 180 GB
tensor parallel
5313all 192K176 GB a card
4× RTX PRO 6000 Blackwell 96 GB
tensor parallel
5914all 192K93.8 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
1233 GB239 GB
5241 GB272 GB
8247 GB297 GB
16264 GB364 GB
32297 GB497 GB
64364 GB763 GB

One card, with vLLM's small-card settings.

From the model card

What MiniMax says about MiniMax-M2.1

Read the model card

Join Our 💬 WeChat 🧩 Discord community. MiniMax Agent ⚡️ API MCP MiniMax Website 🤗 Hugging Face 🐙 GitHub 🤖️ ModelScope 📄 License: Modified-MIT

Meet MiniMax-M2.1

Today, we are handing MiniMax-M2.1 over to the open-source community. This release is more than just a parameter update; it is a significant step toward democratizing top-tier agentic capabilities.

M2.1 was built to shatter the stereotype that high-performance agents must remain behind closed doors. We have optimized the model specifically for robustness in coding, tool use, instruction following, and long-horizon planning. From automating multilingual software development to executing complex, multi-step office workflows, MiniMax-M2.1 empowers developers to build the next generation of autonomous applications—all while being fully transparent, controllable, and accessible.

We believe true intelligence should be within reach. M2.1 is our commitment to the future, and a powerful new tool in your hands.

How to Use

  • The MiniMax-M2.1 API is now live on the MiniMax Open Platform: https://platform.minimax.io/docs/guides/text-generation
  • Our product MiniMax Agent, built on MiniMax-M2.1, is now publicly available: https://agent.minimax.io/
  • The MiniMax-M2.1 model weights are now open-source, allowing for local deployment and use: https://huggingface.co/MiniMaxAI/MiniMax-M2.1

Benchmarks

MiniMax-M2.1 delivers a significant leap over M2 on core software engineering leaderboards. It shines particularly bright in multilingual scenarios, where it outperforms Claude Sonnet 4.5 and closely approaches Claude Opus 4.5.

BenchmarkMiniMax-M2.1MiniMax-M2Claude Sonnet 4.5Claude Opus 4.5Gemini 3 ProGPT-5.2 (thinking)DeepSeek V3.2
SWE-bench Verified74.069.477.280.978.080.073.1
Multi-SWE-bench49.436.244.350.042.7x37.4
SWE-bench Multilingual72.556.56877.565.072.070.2
Terminal-bench 2.047.930.050.057.854.254.046.4

We also evaluated MiniMax-M2.1 on SWE-bench Verified across a variety of coding agent frameworks. The results highlight the model's exceptional framework generalization and robust stability.

Furthermore, across specific benchmarks—including test case generation, code performance optimization, code review, and instruction following—MiniMax-M2.1 demonstrates comprehensive improvements over M2. In these specialized domains, it consistently matches or exceeds the performance of Claude Sonnet 4.5.

BenchmarkMiniMax-M2.1MiniMax-M2Claude Sonnet 4.5Claude Opus 4.5Gemini 3 ProGPT-5.2 (thinking)DeepSeek V3.2
SWE-bench Verified (Droid)71.368.172.375.2xx67.0
SWE-bench Verified (mini-swe-agent)67.061.070.674.471.874.260.0
SWT-bench69.332.869.580.279.780.762.0
SWE-Perf3.11.43.04.76.53.60.9
SWE-Review8.93.410.516.2xx6.4
OctoCodingbench26.113.322.836.222.9x26.0

To evaluate the model's full-stack capability to architect complete, functional applications "from zero to one," we established a novel benchmark: VIBE (Visual & Interactive Benchmark for Execution in Application Development). This suite encompasses five core subsets: Web, Simulation, Android, iOS, and Backend. Distinguishing itself from traditional benchmarks, VIBE leverages an innovative Agent-as-a-Verifier (AaaV) paradigm to automatically assess the interactive logic and visual aesthetics of generated applications within a real runtime environment.

MiniMax-M2.1 delivers outstanding performance on the VIBE aggregate benchmark, achieving an average score of 88.6—demonstrating robust full-stack development capabilities. It excels particularly in the VIBE-Web (91.5) and VIBE-Android (89.7) subsets.

BenchmarkMiniMax-M2.1MiniMax-M2Claude Sonnet 4.5Claude Opus 4.5Gemini 3 Pro
VIBE (Average)88.667.585.290.782.4
VIBE-Web91.580.487.389.189.5
VIBE-Simulation87.177.079.184.089.2
VIBE-Android89.769.287.592.278.7
VIBE-iOS88.039.581.290.075.8
VIBE-Backend86.767.890.898.078.7

MiniMax-M2.1 also demonstrates steady improvements over M2 in both long-horizon tool use and comprehensive intelligence metrics.

BenchmarkMiniMax-M2.1MiniMax-M2Claude Sonnet 4.5Claude Opus 4.5Gemini 3 ProGPT-5.2 (thinking)DeepSeek V3.2
Toolathlon43.516.738.943.536.441.735.2
BrowseComp47.444.019.637.037.865.851.4
BrowseComp (context management)62.056.926.157.859.270.067.6
AIME2583.078.088.091.096.098.092.0
MMLU-Pro88.082.088.090.090.087.086.0
GPQA-D83.078.083.087.091.090.084.0
HLE w/o tools22.212.517.328.437.231.422.2
LCB81.083.071.087.092.089.086.0
SciCode41.036.045.050.056.052.039.0
IFBench70.072.057.058.070.075.061.0
AA-LCR62.061.066.074.071.073.065.0
𝜏²-Bench Telecom87.087.078.090.087.085.091.0

Evaluation Methodology Notes:

  • SWE-bench Verified: Tested on internal infrastructure using Claude Code, Droid, or mini-swe-agent as scaffolding. By default, we utilized Claude Code metrics. When using Claude Code, the def

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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