MiniMax M2.5 leads on the shared-benchmark average
Ahead on 8 of 9 shared benchmarks, averaging 9.4 points higher
Summarised only from the 9 percentage-scale benchmarks scored by every selected model; details are below.
“Best available” takes each model’s best recorded non-parallel result in the metric’s stated direction per benchmark, so it may combine modes into a virtual configuration that does not exist. Read it with the mode breakdown.

MiniMax M2
MiniMaxAI
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
“Best available” picks the best non-parallel result in the metric’s stated direction separately for each benchmark. The resulting series can combine several reasoning levels and is not one reproducible runtime configuration. Choose a mode filter to compare like-for-like runs.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: 指令跟随 +1.5 / Relative gap: 编程与软件工程 -10.8
Relative edge: 编程与软件工程 +10.8 / Relative gap: 指令跟随 -1.5
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
9 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | MiniMax M2 | MiniMax M2.5 |
|---|---|---|
0.90Thinking Enabled | 1.10Thinking Enabled | |
13.70Thinking Enabled | 20.50Thinking Enabled | |
78.00Thinking Enabled | 85.20Thinking Enabled | |
69.40Thinking Enabled | Tools | 80.20Thinking Enabled | Tools | |
78.00Thinking Enabled | 86.30Thinking Enabled | |
25.80Thinking Enabled | Tools | 34.80Thinking Enabled | Tools | |
87.00Thinking Enabled | Tools | 97.80Thinking Enabled | Tools | |
72.30Thinking Enabled | 71.60Thinking Enabled | |
44.00Thinking Enabled | Tools | 76.30Thinking Enabled | Tools |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | MiniMax M2MiniMaxAI | MiniMax M2.5MiniMaxAI |
|---|---|---|
Core specsRelease | 2025-10-27 | 2026-02-12 |
Context length | 205K | 128K |
Total parameters | 230B | 229B |
Active parameters | 10B | 10B |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 关闭开启 | 关闭开启 |
Availability & licensingCode availability | Available · MIT License | Available · MIT License |
Weight availability | Available · MIT License | Available · MINIMAX MODEL LICENSE |
Use & commercial terms | 免费商用授权 | 免费商用授权 |
Local deploymentWeight size | 239.99 GB | 230GB |
VRAM for weights | ≈ 240 GB (weights only, excludes KV cache) | ≈ 230 GB (weights only, excludes KV cache) |
Weights | Hugging Face | Hugging Face |
Source repo | GitHub | GitHub |
API modality supportText Input/Output | Input:YesOutput:Yes | Input:YesOutput:Yes |
ResourcesPaper / report | Not provided | MiniMax M2.5: Built for Real-World Productivity. |
DataLearner blog | MiniMaxAI开源MiniMax M2模型:Artificial Analysis评测显示综合智能得分超过Claude Opus 4.1,开源第一,全球第五。 | Not provided |

MiniMax M2.5
MiniMaxAI