Mathematics · Zhipu AI

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

GLM-4.5 Instruct scores 79.6 on MATH-500, placing it #217 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
79.6
0–100 scale
Rank
#217
of 600
Percentile
64th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
17.1
points

Score distribution

The chart shows the top 10 models on MATH-500, with GLM-4.5 Instruct 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 Instruct sits at #217 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 17.1 points. The median model in this index scores 76.0, which puts GLM-4.5 Instruct above the midpoint.

Zhipu AI models average 73.2 on this benchmark. GLM-4.5 Instruct 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 81.3. GLM-4.5 Instruct is 1.7 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 79.6 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 $4.70 per million input tokens, GLM-4.5 Instruct is above 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
Llama 3 Replicate Instruct Replicate 128000 $3.71 75.1
Llama 3 Lambda Lambda 65536 $3.90 75.6
Llama 3.1 Turbo Together AI 200000 $4.17 76.8
Nova Lite Max Amazon 200000 $9.79 73.4
GLM-4.5 Instruct Zhipu AI 200000 $4.70 78.8
Llama 3 Anyscale Instruct Anyscale 200000 $3.91 75.7
MiniMax-Text Base MiniMax 128000 $3.66 74.9

Other benchmarks for GLM-4.5 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 82.1 119 80th
GPQA Diamond 75.3 139 77th
SWE-bench Verified 67.3 161 73th
HumanEval 82.2 180 70th
AIME 2025 74.3 168 72th
LiveCodeBench 74.8 97 84th
IFEval 84.4 202 66th
TruthfulQA 83.4 96 84th
HellaSwag 87.8 149 75th
MMMU 75.4 153 75th
Arena Elo 1410.6 183 70th

FAQ

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

GLM-4.5 Instruct scores 79.6 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

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

It ranks #217 of 600 models, in the 64th percentile. The leader is GPT-5.2 Max at 96.7.

Is GLM-4.5 Instruct 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 Instruct cost?

GLM-4.5 Instruct costs $4.70 per million input tokens and $22.79 per million output tokens.

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