Multimodal · Zhipu AI

GLM-4.5 on MMMU: score, rank and what it means

GLM-4.5 scores 73.9 on MMMU, placing it #194 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
73.9
0–100 scale
Rank
#194
of 600
Percentile
68th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
13.0
points

Score distribution

The chart shows the top 10 models on MMMU, with GLM-4.5 highlighted. Longer bars mean higher scores.

GPT-5.2 Max 86.9
GPT-5.1 Reasoning 86.2
GPT-5.2 85.3
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.1 Max 85.1
Grok 4 Pro 84.9
Claude Opus 4.5 Reasoning 84.6
Claude Sonnet 4.5 Pro 84.5
GPT-5.2 Pro 84.4
GPT-5.2 Vision 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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-4.5 sits at #194 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 13.0 points. The median model in this index scores 70.0, which puts GLM-4.5 above the midpoint.

Zhipu AI models average 67.5 on this benchmark. GLM-4.5 is 6.4 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-4.5 is 0.4 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 73.9 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 $4.55 per million input tokens, GLM-4.5 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.

ModelProviderContextInput / 1MAvg score
Qwen3-Coder Instruct Alibaba Cloud 128000 $4.66 78.1
Hunyuan-Large Chat Tencent 200000 $4.35 78.1
MiniMax-Text MiniMax 32768 $4.44 77.0
o3 Mini OpenAI 128000 $10.68 76.2
GLM-4.5 Zhipu AI 200000 $4.55 78.2
GPT-4.1 Mini OpenAI 128000 $10.43 74.3
Claude 3.7 Sonnet Vision Anthropic 400000 $13.96 81.0

Other benchmarks for GLM-4.5

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.8 215 64th
GPQA Diamond 74.9 147 76th
SWE-bench Verified 66.4 181 70th
HumanEval 81.6 193 68th
MATH-500 81.7 170 72th
AIME 2025 76.0 141 77th
LiveCodeBench 73.0 127 79th
IFEval 85.8 164 73th
TruthfulQA 79.7 193 68th
HellaSwag 88.8 117 81th
Arena Elo 1414.6 169 72th

FAQ

What score does GLM-4.5 get on MMMU?

GLM-4.5 scores 73.9 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does GLM-4.5 rank on MMMU?

It ranks #194 of 600 models, in the 68th percentile. The leader is GPT-5.2 Max at 86.9.

Is GLM-4.5 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-4.5 cost?

GLM-4.5 costs $4.55 per million input tokens and $20.60 per million output tokens.

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