How to Use
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.
Table with columns: Benchmark, MiniMax-M2.1, MiniMax-M2, Claude Sonnet 4.5, Claude Opus 4.5, Gemini 3 Pro, GPT-5.2 (thinking), DeepSeek V3.2| 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 |
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.
Table with columns: Benchmark, MiniMax-M2.1, MiniMax-M2, Claude Sonnet 4.5, Claude Opus 4.5, Gemini 3 Pro, GPT-5.2 (thinking), DeepSeek V3.2| 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 |
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.
Table with columns: Benchmark, MiniMax-M2.1, MiniMax-M2, Claude Sonnet 4.5, Claude Opus 4.5, Gemini 3 Pro| 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 |
MiniMax-M2.1 also demonstrates steady improvements over M2 in both long-horizon tool use and comprehensive intelligence metrics.
Table with columns: Benchmark, MiniMax-M2.1, MiniMax-M2, Claude Sonnet 4.5, Claude Opus 4.5, Gemini 3 Pro, GPT-5.2 (thinking), DeepSeek V3.2| 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 |
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 default system prompt was overridden. Results represent the average of 4 runs.
- Multi-SWE-Bench & SWE-bench Multilingual & SWT-bench & SWE-Perf: Tested on internal infrastructure using Claude Code as scaffolding, with the default system prompt overridden. Results represent the average of 4 runs.
- Terminal-bench 2.0: Tested using Claude Code on our internal evaluation framework. We verified the full dataset and fixed environmental issues. Timeout limits were removed, while all other configurations remained consistent with official settings. Results represent the average of 4 runs.
- SWE Review: Built upon the SWE framework, this internal benchmark for code defect review covers diverse languages and scenarios, evaluating both defect recall and hallucination rates. A review is deemed "correct" only if the model accurately identifies the target defect and ensures all other reported findings are valid and free of hallucinations. All evaluations are executed using Claude Code, with final results reflecting the average of four independent runs per test case. We plan to open-source this benchmark soon.
Local Deployment Guide
Download the model from HuggingFace repository: https://huggingface.co/MiniMaxAI/MiniMax-M2.1
We recommend using the following inference frameworks (listed alphabetically) to serve the model:
SGLang
We recommend using SGLang to serve MiniMax-M2.1. Please refer to our SGLang Deployment Guide.
vLLM
We recommend using vLLM to serve MiniMax-M2.1. Please refer to our vLLM Deployment Guide.
We recommend using Transformers to serve MiniMax-M2.1. Please refer to our Transformers Deployment Guide.
We recommend using KTransformers to serve MiniMax-M2.1. Please refer to KTransformers Deployment Guide
Other Inference Engines
Inference Parameters
We recommend using the following parameters for best performance: temperature=1.0, top_p = 0.95, top_k = 40. Default system prompt:
You are a helpful assistant. Your name is MiniMax-M2.1 and is built by MiniMax.
Please refer to our Tool Calling Guide.
Contact us at model@minimax.io.