GLM-5 leads on the shared-benchmark average
Ahead on 8 of 12 shared benchmarks, averaging 1.0 points higher
Summarised only from the 12 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.

GLM-5
智谱AI
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 +3.2 / Relative gap: 科学与综合推理 -8.9
Relative edge: 科学与综合推理 +8.9 / Relative gap: Agent能力评测 -3.2
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.
17 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | GLM-5 | MiniMax M2.5 |
|---|---|---|
44.67Thinking Enabled | 63.67Thinking Enabled | |
4.86Thinking Enabled | 4.86Thinking Enabled | |
2.00Thinking Enabled | 1.10Thinking Enabled | |
50.40Thinking Enabled | Tools | 20.50Thinking Enabled | |
68.85Standard Mode | 60.14Deep Thinking Mode | |
86.00Thinking Enabled | 85.20Thinking Enabled | |
77.80Thinking Enabled | 80.20Thinking Enabled | Tools | |
1597.50Standard Mode | 1358.40Standard Mode | |
43.00Thinking Enabled | Tools | 34.80Thinking Enabled | Tools | |
98.00Thinking Enabled | Tools | 97.80Thinking Enabled | Tools | |
72.30Thinking Enabled | 71.60Thinking Enabled | |
75.90Thinking Enabled | Tools | 76.30Thinking Enabled | Tools | |
61.10Thinking Enabled | Tools | 51.70Thinking Enabled | Tools | |
46.00Thinking Enabled | 36.00Thinking Enabled | |
75.70Thinking Enabled | 73.30Thinking Enabled | |
91.70Thinking Enabled | Tools | 92.10Thinking Enabled | Tools | |
86.40Thinking Enabled | Tools | 87.80Thinking 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 | GLM-5智谱AI | MiniMax M2.5MiniMaxAI |
|---|---|---|
Core specsRelease | 2026-02-11 | 2026-02-12 |
Context length | 200K | 128K |
Total parameters | 744B | 229B |
Active parameters | 40B | 10B |
Max output length | 131,072 tokens | Not provided |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 关闭扩展 | 关闭开启 |
Availability & licensingCode availability | Available · Apache 2.0 | 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 | 1.51TB | 230GB |
VRAM for weights | ≈ 1510 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 | GLM-5: From Vibe Coding to Agentic Engineering | MiniMax M2.5: Built for Real-World Productivity. |

MiniMax M2.5
MiniMaxAI