Model reference · open weights
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.
Summary of the MiniMaxAI/MiniMax-M2.1 model card, 2026-10-01
What it is
| Released by | MiniMax |
|---|---|
| Released | 2025-12-20 |
| Parameters | 228.7B |
| VRAM | 230 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … B200 180 GB 12 smaller cards | — | — | — | |
| 2× H200 141 GB tensor parallel | 16 | 4 | 135K | 138 GB a card |
| 4× H100 80 GB tensor parallel | 29 | 7 | all 192K | 78.1 GB a card |
| 4× A100 80 GB tensor parallel · FP8 without its speed-up here | 38 | 9 | all 192K | 78.2 GB a card |
| 2× B200 180 GB tensor parallel | 53 | 13 | all 192K | 176 GB a card |
| 4× RTX PRO 6000 Blackwell 96 GB tensor parallel | 59 | 14 | all 192K | 93.8 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 233 GB | 239 GB |
| 5 | 241 GB | 272 GB |
| 8 | 247 GB | 297 GB |
| 16 | 264 GB | 364 GB |
| 32 | 297 GB | 497 GB |
| 64 | 364 GB | 763 GB |
One card, with vLLM's small-card settings.
From the model card
Join Our 💬 WeChat 🧩 Discord community. MiniMax Agent ⚡️ API MCP MiniMax Website 🤗 Hugging Face 🐙 GitHub 🤖️ ModelScope 📄 License: Modified-MIT
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.
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.
| Benchmark | MiniMax-M2.1 | MiniMax-M2 | Claude Sonnet 4.5 | Claude Opus 4.5 | Gemini 3 Pro | GPT-5.2 (thinking) | DeepSeek V3.2 |
|---|---|---|---|---|---|---|---|
| SWE-bench Verified | 74.0 | 69.4 | 77.2 | 80.9 | 78.0 | 80.0 | 73.1 |
| Multi-SWE-bench | 49.4 | 36.2 | 44.3 | 50.0 | 42.7 | x | 37.4 |
| SWE-bench Multilingual | 72.5 | 56.5 | 68 | 77.5 | 65.0 | 72.0 | 70.2 |
| Terminal-bench 2.0 | 47.9 | 30.0 | 50.0 | 57.8 | 54.2 | 54.0 | 46.4 |
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.
| Benchmark | MiniMax-M2.1 | MiniMax-M2 | Claude Sonnet 4.5 | Claude Opus 4.5 | Gemini 3 Pro | GPT-5.2 (thinking) | DeepSeek V3.2 |
|---|---|---|---|---|---|---|---|
| SWE-bench Verified (Droid) | 71.3 | 68.1 | 72.3 | 75.2 | x | x | 67.0 |
| SWE-bench Verified (mini-swe-agent) | 67.0 | 61.0 | 70.6 | 74.4 | 71.8 | 74.2 | 60.0 |
| SWT-bench | 69.3 | 32.8 | 69.5 | 80.2 | 79.7 | 80.7 | 62.0 |
| SWE-Perf | 3.1 | 1.4 | 3.0 | 4.7 | 6.5 | 3.6 | 0.9 |
| SWE-Review | 8.9 | 3.4 | 10.5 | 16.2 | x | x | 6.4 |
| OctoCodingbench | 26.1 | 13.3 | 22.8 | 36.2 | 22.9 | x | 26.0 |
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.
| Benchmark | MiniMax-M2.1 | MiniMax-M2 | Claude Sonnet 4.5 | Claude Opus 4.5 | Gemini 3 Pro |
|---|---|---|---|---|---|
| VIBE (Average) | 88.6 | 67.5 | 85.2 | 90.7 | 82.4 |
| VIBE-Web | 91.5 | 80.4 | 87.3 | 89.1 | 89.5 |
| VIBE-Simulation | 87.1 | 77.0 | 79.1 | 84.0 | 89.2 |
| VIBE-Android | 89.7 | 69.2 | 87.5 | 92.2 | 78.7 |
| VIBE-iOS | 88.0 | 39.5 | 81.2 | 90.0 | 75.8 |
| VIBE-Backend | 86.7 | 67.8 | 90.8 | 98.0 | 78.7 |
MiniMax-M2.1 also demonstrates steady improvements over M2 in both long-horizon tool use and comprehensive intelligence metrics.
| Benchmark | MiniMax-M2.1 | MiniMax-M2 | Claude Sonnet 4.5 | Claude Opus 4.5 | Gemini 3 Pro | GPT-5.2 (thinking) | DeepSeek V3.2 |
|---|---|---|---|---|---|---|---|
| Toolathlon | 43.5 | 16.7 | 38.9 | 43.5 | 36.4 | 41.7 | 35.2 |
| BrowseComp | 47.4 | 44.0 | 19.6 | 37.0 | 37.8 | 65.8 | 51.4 |
| BrowseComp (context management) | 62.0 | 56.9 | 26.1 | 57.8 | 59.2 | 70.0 | 67.6 |
| AIME25 | 83.0 | 78.0 | 88.0 | 91.0 | 96.0 | 98.0 | 92.0 |
| MMLU-Pro | 88.0 | 82.0 | 88.0 | 90.0 | 90.0 | 87.0 | 86.0 |
| GPQA-D | 83.0 | 78.0 | 83.0 | 87.0 | 91.0 | 90.0 | 84.0 |
| HLE w/o tools | 22.2 | 12.5 | 17.3 | 28.4 | 37.2 | 31.4 | 22.2 |
| LCB | 81.0 | 83.0 | 71.0 | 87.0 | 92.0 | 89.0 | 86.0 |
| SciCode | 41.0 | 36.0 | 45.0 | 50.0 | 56.0 | 52.0 | 39.0 |
| IFBench | 70.0 | 72.0 | 57.0 | 58.0 | 70.0 | 75.0 | 61.0 |
| AA-LCR | 62.0 | 61.0 | 66.0 | 74.0 | 71.0 | 73.0 | 65.0 |
| 𝜏²-Bench Telecom | 87.0 | 87.0 | 78.0 | 90.0 | 87.0 | 85.0 | 91.0 |
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.