Coding · Zhipu AI

GLM-4.6 Instruct on HumanEval: score, rank and what it means

GLM-4.6 Instruct scores 85.3 on HumanEval, placing it #108 of 600 models in the index. The benchmark average is 74.0 and the leader is GPT-5.2 Pro at 96.8.

Score
85.3
0–100 scale
Rank
#108
of 600
Percentile
82th
within the index
Benchmark avg
74.0
all models
Leader
96.8
GPT-5.2 Pro
Gap to leader
11.5
points

Score distribution

The chart shows the top 10 models on HumanEval, with GLM-4.6 Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 96.8
Gemini 3 Pro Pro 95.0
o4 Max 94.6
Grok 4 Reasoning 93.8
Claude Opus 4.5 93.7
GPT-5.2 Max 93.6
Claude Opus 4.5 Pro 93.4
Grok 4 Max 93.4
GPT-5.2 Turbo 93.2
GPT-5.1 Reasoning 92.9

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Gemini 3 Pro Pro Google DeepMind 200000 $19.80 87.3
3 o4 Max OpenAI 2000000 $19.51 86.2
4 Grok 4 Reasoning xAI 2000000 $19.67 87.8
5 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
6 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
7 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
8 Grok 4 Max xAI 2000000 $19.70 86.6
9 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
10 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3

What this score means

On HumanEval, GLM-4.6 Instruct sits at #108 of 600. The leader, GPT-5.2 Pro, scores 96.8, so the gap to the top is 11.5 points. The median model in this index scores 77.4, which puts GLM-4.6 Instruct above the midpoint.

Zhipu AI models average 74.4 on this benchmark. GLM-4.6 Instruct is 10.9 points above that provider line, so it is a stronger result than the average Zhipu AI model.

Among multimodal models, the average is 82.2. GLM-4.6 Instruct is 3.1 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 85.3 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 $5.15 per million input tokens, GLM-4.6 Instruct is above the median input price of $4.00 across the 600 models scored on HumanEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
o3 Vision OpenAI 1000000 $15.24 81.8
GPT-5.2 Mini OpenAI 200000 $13.34 80.4
Claude 3.7 Sonnet Vision Anthropic 400000 $13.96 81.0
Llama 4 Maverick Base Meta AI 128000 $4.68 79.1
GLM-4.6 Instruct Zhipu AI 1000000 $5.15 80.4
Grok 3 xAI 128000 $15.84 82.8
Reka Core Turbo Reka AI 32768 $12.58 78.4

Other benchmarks for GLM-4.6 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.6 163 73th
GPQA Diamond 75.8 124 79th
SWE-bench Verified 70.5 100 83th
MATH-500 84.9 110 82th
AIME 2025 74.5 164 73th
LiveCodeBench 72.6 134 78th
IFEval 87.4 121 80th
TruthfulQA 83.8 85 86th
HellaSwag 90.6 72 88th
MMMU 78.9 74 88th
Arena Elo 1426.6 126 79th

FAQ

What score does GLM-4.6 Instruct get on HumanEval?

GLM-4.6 Instruct scores 85.3 on HumanEval in this index. The benchmark average is 74.0 and the top score is 96.8.

How does GLM-4.6 Instruct rank on HumanEval?

It ranks #108 of 600 models, in the 82nd percentile. The leader is GPT-5.2 Pro at 96.8.

Is GLM-4.6 Instruct good at HumanEval?

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 HumanEval score calculated?

HumanEval is a coding 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 Instruct cost?

GLM-4.6 Instruct costs $5.15 per million input tokens and $24.32 per million output tokens.

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