Noometry

Model comparison

GLM-4.6V vs Qwen2.5-Coder-32B

GLM-4.6V is the stronger model overall, scoring 41.3 to 33.4 on the Noometry Index.

Last verified . 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

Qwen2.5-Coder-32B Alibaba (Qwen)

33.4

Rank #245 Confirmed

Summary

  • They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Qwen2.5-Coder-32B in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-4.6V leads 40.9 to 22.6.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
  • GLM-4.6V accepts more context: 128K tokens versus 33K.

Side by side

GLM-4.6VQwen2.5-Coder-32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.333.4
Released2025-12-082024-09-18
WeightsOpenOpen
Context window128K33K
Max output33K29K
Input $ / M tokens$0.30$0.66
Output $ / M tokens$0.90$1
Results tracked1231

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.9 (#128), Qwen2.5-Coder-32B: 22.6 (#333)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Coding13901276
SWE-bench Verified (bash only)—9%
Aider Polyglot—16.4%
BigCodeBench Instruct—49%
LiveBench Coding—56.9%
BigCodeBench Complete—58%
HumanEval+—87.2%
MBPP+—77%

Reasoning GLM-4.6V leads

GLM-4.6V: 27.6 (#115), Qwen2.5-Coder-32B: 21.2 (#225)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Hard Prompts13681251
LiveBench Reasoning—42.1%
LiveBench Data Analysis—49.9%
Epoch Capabilities Index—119.49
HellaSwag—83%
LiveBench—46.2%
WinoGrande—80.8%

Math Not comparable

GLM-4.6V: —, Qwen2.5-Coder-32B: 33.3 (#204)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LiveBench Math—46.6%
LMArena Math—1251
GSM8K—93%

Knowledge GLM-4.6V leads

GLM-4.6V: 38.0 (#149), Qwen2.5-Coder-32B: 33.4 (#203)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Expert13711221
ARC (AI2) Challenge—70.5%
MMLU—79.1%

Multimodal Not comparable

GLM-4.6V: 34.8 (#90), Qwen2.5-Coder-32B: —

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Vision1164—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.6 (#141), Qwen2.5-Coder-32B: 37.8 (#235)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Non-English13591205
LMArena Chinese14251222
LMArena Russian13401228

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.4 (#151), Qwen2.5-Coder-32B: 61.4 (#245)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Instruction Following13521223
LiveBench Instruction Following—58.7%

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#143), Qwen2.5-Coder-32B: 38.0 (#208)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Longer Query13581251

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.6 (#137), Qwen2.5-Coder-32B: 41.6 (#240)

BenchmarkGLM-4.6VQwen2.5-Coder-32B
LMArena Text13771230
LMArena Creative Writing13471174
LMArena Multi-Turn13601222
LiveBench Language—23.3%

Frequently asked questions

Is GLM-4.6V better than Qwen2.5-Coder-32B?

GLM-4.6V is the stronger model overall, scoring 41.3 to 33.4 on the Noometry Index.

Which is cheaper, GLM-4.6V or Qwen2.5-Coder-32B?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.

Is GLM-4.6V or Qwen2.5-Coder-32B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 22.6 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6V does, with 128K tokens against 33K.

How many benchmarks do GLM-4.6V and Qwen2.5-Coder-32B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen2.5-Coder-32B has 31.

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