GLM-4.6 on MMMU: score, rank and what it means
GLM-4.6 scores 75.4 on MMMU, placing it #154 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.
Score distribution
The chart shows the top 10 models on MMMU, with GLM-4.6 highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 4 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 5 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 6 | Grok 4 Pro | xAI | 1000000 | $19.00 | 85.4 |
| 7 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 8 | Claude Sonnet 4.5 Pro | Anthropic | 1000000 | $18.73 | 86.7 |
| 9 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 10 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
What this score means
On MMMU, GLM-4.6 sits at #154 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 11.5 points. The median model in this index scores 70.0, which puts GLM-4.6 above the midpoint.
Zhipu AI models average 67.5 on this benchmark. GLM-4.6 is 7.9 points above that provider line, so it is a stronger result than the average Zhipu AI model.
Among multimodal models, the average is 74.3. GLM-4.6 is 1.1 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 75.4 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $5.10 per million input tokens, GLM-4.6 is above the median input price of $4.00 across the 600 models scored on MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| DeepSeek-Coder Instruct | DeepSeek | 32768 | $4.34 | 77.7 |
| Reka Core Reasoning | Reka AI | 32768 | $13.11 | 78.5 |
| Command A Turbo | Cohere | 32768 | $13.53 | 79.8 |
| GLM-4.5 Instruct | Zhipu AI | 200000 | $4.70 | 78.8 |
| GLM-4.6 | Zhipu AI | 128000 | $5.10 | 80.2 |
| Llama 4 Maverick Chat | Meta AI | 128000 | $5.09 | 79.7 |
| Sonar Max | Perplexity | 32768 | $11.60 | 77.8 |
Other benchmarks for GLM-4.6
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 81 | 87th | |
| GPQA Diamond | 78.3 | 77 | 87th | |
| SWE-bench Verified | 67.4 | 157 | 74th | |
| HumanEval | 83.8 | 138 | 77th | |
| MATH-500 | 83.4 | 137 | 77th | |
| AIME 2025 | 80.2 | 86 | 86th | |
| LiveCodeBench | 71.6 | 157 | 74th | |
| IFEval | 86.4 | 147 | 76th | |
| TruthfulQA | 82.5 | 118 | 80th | |
| HellaSwag | 89.4 | 102 | 83th | |
| Arena Elo | 1440.7 | 96 | 84th |
FAQ
What score does GLM-4.6 get on MMMU?
GLM-4.6 scores 75.4 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.
How does GLM-4.6 rank on MMMU?
It ranks #154 of 600 models, in the 74th percentile. The leader is GPT-5.2 Max at 86.9.
Is GLM-4.6 good at MMMU?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the MMMU score calculated?
MMMU is a multimodal benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does GLM-4.6 cost?
GLM-4.6 costs $5.10 per million input tokens and $18.45 per million output tokens.