Mathematics · Zhipu AI

GLM-4.5 7B on MATH-500: score, rank and what it means

GLM-4.5 7B scores 63.3 on MATH-500, placing it #433 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
63.3
0–100 scale
Rank
#433
of 600
Percentile
28th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
33.4
points

Score distribution

The chart shows the top 10 models on MATH-500, with GLM-4.5 7B highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

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-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, GLM-4.5 7B sits at #433 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 33.4 points. The median model in this index scores 76.0, which puts GLM-4.5 7B below the midpoint.

Zhipu AI models average 73.2 on this benchmark. GLM-4.5 7B is 9.9 points below that provider line, so it is a weaker result than the average Zhipu AI model.

Among small open-weight models, the average is 53.7. GLM-4.5 7B is 9.6 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 63.3 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 $1.32 per million input tokens, GLM-4.5 7B is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Pythia Instruct EleutherAI 8192 $1.41 60.8
Qwen3-Coder 8B Alibaba Cloud 32768 $1.43 61.3
ERNIE 4.0 Nano Baidu 32768 $4.68 62.1
Fusion Mini OpenRouter 4096 $4.89 63.3
GLM-4.5 7B Zhipu AI 8192 $1.32 60.2
Mistral Large 3 7B Mistral AI 32768 $1.44 61.1
Firefunction Mini Fireworks AI 32768 $4.63 63.2

Other benchmarks for GLM-4.5 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 65.1 452 25th
GPQA Diamond 54.5 472 21th
SWE-bench Verified 45.4 462 23th
HumanEval 65.3 438 27th
AIME 2025 51.2 448 25th
LiveCodeBench 49.4 454 24th
IFEval 70.5 448 25th
TruthfulQA 64.4 465 23th
HellaSwag 73.1 476 21th
MMMU 59.6 432 28th
Arena Elo 1258.6 477 21th

FAQ

What score does GLM-4.5 7B get on MATH-500?

GLM-4.5 7B scores 63.3 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does GLM-4.5 7B rank on MATH-500?

It ranks #433 of 600 models, in the 28th percentile. The leader is GPT-5.2 Max at 96.7.

Is GLM-4.5 7B good at MATH-500?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.

How is the MATH-500 score calculated?

MATH-500 is a mathematics 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 7B cost?

GLM-4.5 7B costs $1.32 per million input tokens and $5.93 per million output tokens.

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