Commonsense · Zhipu AI

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

GLM-4.5 scores 88.8 on HellaSwag, placing it #117 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
88.8
0–100 scale
Rank
#117
of 600
Percentile
81th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
7.3
points

Score distribution

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

GPT-5.2 Max 96.1
GPT-5.2 Turbo 95.5
GPT-5.2 Pro 95.3
Claude Opus 4.5 Pro 95.2
Claude Sonnet 4.5 Reasoning 95.2
Grok 4 Reasoning 95.2
GPT-5.2 Vision 94.6
Gemini 3 Pro Turbo 94.6
GPT-5.1 Reasoning 94.4
o4 Pro 94.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
8 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
9 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
10 o4 Pro OpenAI 1000000 $19.37 87.3

What this score means

On HellaSwag, GLM-4.5 sits at #117 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 7.3 points. The median model in this index scores 83.6, which puts GLM-4.5 above the midpoint.

Zhipu AI models average 81.9 on this benchmark. GLM-4.5 is 6.9 points above that provider line, so it is a stronger result than the average Zhipu AI model.

Among multimodal models, the average is 86.8. GLM-4.5 is 2.0 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 88.8 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 $4.55 per million input tokens, GLM-4.5 is above the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Doubao 1.5 Pro ByteDance 200000 $13.65 79.7
Llama 4 Scout Meta AI 200000 $4.90 79.3
Nova Pro Max Amazon 1000000 $14.05 81.2
DeepSeek-V3.2 Instruct DeepSeek 128000 $5.91 83.7
GLM-4.5 Zhipu AI 200000 $4.55 78.2
GPT-5.1 Mini OpenAI 128000 $12.19 77.7
Llama 4 Maverick Chat Meta AI 128000 $5.09 79.7

Other benchmarks for GLM-4.5

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.8 215 64th
GPQA Diamond 74.9 147 76th
SWE-bench Verified 66.4 181 70th
HumanEval 81.6 193 68th
MATH-500 81.7 170 72th
AIME 2025 76.0 141 77th
LiveCodeBench 73.0 127 79th
IFEval 85.8 164 73th
TruthfulQA 79.7 193 68th
MMMU 73.9 194 68th
Arena Elo 1414.6 169 72th

FAQ

What score does GLM-4.5 get on HellaSwag?

GLM-4.5 scores 88.8 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does GLM-4.5 rank on HellaSwag?

It ranks #117 of 600 models, in the 81st percentile. The leader is GPT-5.2 Max at 96.1.

Is GLM-4.5 good at HellaSwag?

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 HellaSwag score calculated?

HellaSwag is a commonsense 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 cost?

GLM-4.5 costs $4.55 per million input tokens and $20.60 per million output tokens.

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