Commonsense · Zhipu AI

GLM-4.6 Base on HellaSwag: score, rank and what it means

GLM-4.6 Base scores 86.6 on HellaSwag, placing it #190 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
86.6
0–100 scale
Rank
#190
of 600
Percentile
68th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
9.5
points

Score distribution

The chart shows the top 10 models on HellaSwag, with GLM-4.6 Base 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-4.6 Base sits at #190 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 9.5 points. The median model in this index scores 83.6, which puts GLM-4.6 Base above the midpoint.

Zhipu AI models average 81.9 on this benchmark. GLM-4.6 Base is 4.7 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-4.6 Base is 0.2 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 86.6 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 $4.53 per million input tokens, GLM-4.6 Base 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
Llama 3.3 Groq Groq 32768 $3.98 76.8
ERNIE 4.0 Reasoning Baidu 65536 $12.20 77.8
Qwen3-Coder Alibaba Cloud 200000 $4.94 79.5
Yi-Lightning Chat 01.AI 32768 $3.79 75.5
GLM-4.6 Base Zhipu AI 128000 $4.53 78.5
ERNIE 4.0 Pro Baidu 200000 $12.58 77.5
Llama 3 Replicate Replicate 200000 $3.75 75.7

Other benchmarks for GLM-4.6 Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 79.5 194 68th
GPQA Diamond 75.1 141 77th
SWE-bench Verified 66.0 192 68th
HumanEval 83.8 139 77th
MATH-500 83.8 127 79th
AIME 2025 71.8 222 63th
LiveCodeBench 74.5 99 84th
IFEval 86.9 134 78th
TruthfulQA 81.9 131 78th
MMMU 73.2 211 65th
Arena Elo 1421.8 140 77th

FAQ

What score does GLM-4.6 Base get on HellaSwag?

GLM-4.6 Base scores 86.6 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does GLM-4.6 Base rank on HellaSwag?

It ranks #190 of 600 models, in the 68th percentile. The leader is GPT-5.2 Max at 96.1.

Is GLM-4.6 Base 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-4.6 Base cost?

GLM-4.6 Base costs $4.53 per million input tokens and $20.43 per million output tokens.

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