Coding · Zhipu AI

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

GLM-4.5 Chat scores 83.8 on HumanEval, placing it #137 of 600 models in the index. The benchmark average is 74.0 and the leader is GPT-5.2 Pro at 96.8.

Score
83.8
0–100 scale
Rank
#137
of 600
Percentile
77th
within the index
Benchmark avg
74.0
all models
Leader
96.8
GPT-5.2 Pro
Gap to leader
13.0
points

Score distribution

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

GPT-5.2 Pro 96.8
Gemini 3 Pro Pro 95.0
o4 Max 94.6
Grok 4 Reasoning 93.8
Claude Opus 4.5 93.7
GPT-5.2 Max 93.6
Claude Opus 4.5 Pro 93.4
Grok 4 Max 93.4
GPT-5.2 Turbo 93.2
GPT-5.1 Reasoning 92.9

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Gemini 3 Pro Pro Google DeepMind 200000 $19.80 87.3
3 o4 Max OpenAI 2000000 $19.51 86.2
4 Grok 4 Reasoning xAI 2000000 $19.67 87.8
5 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
6 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
7 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
8 Grok 4 Max xAI 2000000 $19.70 86.6
9 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
10 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3

What this score means

On HumanEval, GLM-4.5 Chat sits at #137 of 600. The leader, GPT-5.2 Pro, scores 96.8, so the gap to the top is 13.0 points. The median model in this index scores 77.4, which puts GLM-4.5 Chat above the midpoint.

Zhipu AI models average 74.4 on this benchmark. GLM-4.5 Chat is 9.4 points above that provider line, so it is a stronger result than the average Zhipu AI model.

Among multimodal models, the average is 82.2. GLM-4.5 Chat is 1.6 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 83.8 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 HumanEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Kimi K1.5 Instruct Moonshot AI 200000 $5.15 80.5
ERNIE 4.5 Baidu 65536 $13.44 80.0
MiniMax-Text Chat MiniMax 128000 $4.26 77.8
Qwen3 Chat Alibaba Cloud 128000 $4.75 79.1
GLM-4.5 Chat Zhipu AI 65536 $4.53 78.3
GLM-4.6 Zhipu AI 128000 $5.10 80.2
GLM-4.6 Base Zhipu AI 128000 $4.53 78.5

Other benchmarks for GLM-4.5 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.7 130 78th
GPQA Diamond 74.3 159 74th
SWE-bench Verified 69.6 121 80th
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 HumanEval?

GLM-4.5 Chat scores 83.8 on HumanEval in this index. The benchmark average is 74.0 and the top score is 96.8.

How does GLM-4.5 Chat rank on HumanEval?

It ranks #137 of 600 models, in the 77th percentile. The leader is GPT-5.2 Pro at 96.8.

Is GLM-4.5 Chat good at HumanEval?

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 HumanEval score calculated?

HumanEval is a coding 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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