Knowledge · Zhipu AI

GLM-4.5 Chat on MMLU-Pro: score, rank and what it means

GLM-4.5 Chat scores 81.7 on MMLU-Pro, placing it #130 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
81.7
0–100 scale
Rank
#130
of 600
Percentile
78th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
9.9
points

Score distribution

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

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, GLM-4.5 Chat sits at #130 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 9.9 points. The median model in this index scores 76.1, which puts GLM-4.5 Chat above the midpoint.

Zhipu AI models average 73.5 on this benchmark. GLM-4.5 Chat is 8.2 points above that provider line, so it is a stronger result than the average Zhipu AI model.

Among multimodal models, the average is 80.0. GLM-4.5 Chat is 1.7 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 81.7 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 $4.53 per million input tokens, GLM-4.5 Chat is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Kimi K2 Base Moonshot AI 65536 $4.82 79.2
MiniMax-M2 MiniMax 200000 $5.19 80.6
Nova Pro Turbo Amazon 128000 $13.18 78.4
ERNIE 4.0 Pro Baidu 200000 $12.58 77.5
GLM-4.5 Chat Zhipu AI 65536 $4.53 78.3
ERNIE 4.0 Reasoning Baidu 65536 $12.20 77.8
Sonar Pro Turbo Perplexity 128000 $13.55 79.0

Other benchmarks for GLM-4.5 Chat

BenchmarkScoreRankPercentileRelative
GPQA Diamond 74.3 159 74th
SWE-bench Verified 69.6 121 80th
HumanEval 83.8 137 77th
MATH-500 82.8 149 75th
AIME 2025 70.7 243 60th
LiveCodeBench 68.9 209 65th
IFEval 84.5 201 67th
TruthfulQA 81.6 139 77th
HellaSwag 88.1 138 77th
MMMU 75.6 148 75th
Arena Elo 1418.6 155 74th

FAQ

What score does GLM-4.5 Chat get on MMLU-Pro?

GLM-4.5 Chat scores 81.7 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does GLM-4.5 Chat rank on MMLU-Pro?

It ranks #130 of 600 models, in the 78th percentile. The leader is GPT-5.2 Pro at 91.6.

Is GLM-4.5 Chat good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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 Chat cost?

GLM-4.5 Chat costs $4.53 per million input tokens and $21.22 per million output tokens.

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