Mathematics · Zhipu AI

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

GLM-4.5 Base scores 77.5 on MATH-500, placing it #265 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
77.5
0–100 scale
Rank
#265
of 600
Percentile
56th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
19.2
points

Score distribution

The chart shows the top 10 models on MATH-500, with GLM-4.5 Base 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 Base sits at #265 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 19.2 points. The median model in this index scores 76.0, which puts GLM-4.5 Base above the midpoint.

Zhipu AI models average 73.2 on this benchmark. GLM-4.5 Base is 4.3 points above that provider line, so it is a stronger result than the average Zhipu AI model.

Among multimodal models, the average is 81.3. GLM-4.5 Base is 3.8 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 77.5 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 $3.99 per million input tokens, GLM-4.5 Base 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
Fusion Turbo OpenRouter 8192 $8.55 71.7
Nemotron 4 NVIDIA 65536 $4.18 76.6
Llama 3 DeepInfra Instruct DeepInfra 65536 $3.84 75.5
Nova Lite Turbo Amazon 8192 $9.14 73.2
GLM-4.5 Base Zhipu AI 32768 $3.99 74.9
QwQ Base Alibaba Cloud 65536 $3.68 74.1
Codestral Base Mistral AI 8192 $3.31 73.1

Other benchmarks for GLM-4.5 Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.1 302 50th
GPQA Diamond 70.6 244 59th
SWE-bench Verified 61.6 289 52th
HumanEval 78.7 270 55th
AIME 2025 68.6 286 52th
LiveCodeBench 71.5 160 73th
IFEval 83.3 236 61th
TruthfulQA 76.5 291 52th
HellaSwag 84.6 261 57th
MMMU 74.4 172 71th
Arena Elo 1403.0 202 66th

FAQ

What score does GLM-4.5 Base get on MATH-500?

GLM-4.5 Base scores 77.5 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does GLM-4.5 Base rank on MATH-500?

It ranks #265 of 600 models, in the 56th percentile. The leader is GPT-5.2 Max at 96.7.

Is GLM-4.5 Base good at MATH-500?

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 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 Base cost?

GLM-4.5 Base costs $3.99 per million input tokens and $20.65 per million output tokens.

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