MiniMax M2.5 leads on the shared-benchmark average
Ahead on 7 of 14 shared benchmarks, averaging 0.1 points higher
Summarised only from the 14 percentage-scale benchmarks scored by every selected model; details are below. A further 2 historical, reverse-direction or differently scaled metrics are excluded; indexes, cost and time are not added to accuracy percentages.
“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 M2.5
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: Agent能力评测 +8.6 / Relative gap: 数学推理 -9.8
Relative edge: 数学推理 +9.8 / Relative gap: Agent能力评测 -8.6
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.
19 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | MiniMax M2.5 | Kimi K2.5 |
|---|---|---|
63.67Thinking Enabled | 65.33Thinking Enabled | |
4.86Thinking Enabled | 11.81Thinking Enabled | |
1.10Thinking Enabled | 3.10Thinking Enabled | |
20.50Thinking Enabled | 50.20Thinking Enabled | Tools | |
60.14Deep Thinking Mode | 69.07Thinking Enabled | |
85.20Thinking Enabled | 87.60Thinking Enabled | |
55.40Thinking Enabled | Tools | 50.70Thinking Enabled | Tools | |
80.20Thinking Enabled | Tools | 76.80Thinking Enabled | Tools | |
86.30Thinking Enabled | 96.10Thinking Enabled | |
1358.40Standard Mode | 1575.80Standard Mode | |
34.80Thinking Enabled | Tools | 34.80Thinking Enabled | Tools | |
97.80Thinking Enabled | Tools | 95.90Thinking Enabled | Tools | |
71.60Thinking Enabled | 70.20Thinking Enabled | |
76.30Thinking Enabled | Tools | 60.60Thinking Enabled | Tools | |
51.70Thinking Enabled | Tools | 50.80Thinking Enabled | Tools | |
36.00Thinking Enabled | 40.00Thinking Enabled | |
73.30Thinking Enabled | 78.00Thinking Enabled | |
92.10Thinking Enabled | Tools | 81.70Thinking Enabled | Tools | |
87.80Thinking Enabled | Tools | 84.80Thinking 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 M2.5MiniMaxAI | Kimi K2.5Moonshot AI |
|---|---|---|
Core specsRelease | 2026-02-12 | 2026-01-27 |
Context length | 128K | 256K |
Total parameters | 229B | 1T |
Active parameters | 10B | 32B |
Max output length | Not provided | 16,384 tokens |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 关闭开启 | 关闭开启扩展 |
Availability & licensingCode availability | Available · MIT License | Available · Modified MIT License |
Weight availability | Available · MINIMAX MODEL LICENSE | Available · Modified MIT License |
Use & commercial terms | 免费商用授权 | 免费商用授权 |
Local deploymentWeight size | 230GB | 595GB |
VRAM for weights | ≈ 230 GB (weights only, excludes KV cache) | ≈ 595 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 |
Image Input/Output | Input:NoOutput:No | Input:YesOutput:No |
ResourcesPaper / report | MiniMax M2.5: Built for Real-World Productivity. | Kimi K2.5: Visual Agentic Intelligence |
DataLearner blog | Not provided | 重磅!Kimi K2.5发布,依然免费开源!原生多模态MoE架构,全球最大规模参数的开源模型之一,官方评测结果比肩诸多闭源模型!可以驱动100个子Agent执行! |

Kimi K2.5
Moonshot AI