Noometry

Model comparison

GLM-4.5V vs Qwen2.5-Coder-32B

GLM-4.5V is the stronger model overall, scoring 39.2 to 32.9 on the Noometry Index.

Last verified . 12 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.2

Rank #158 Confirmed

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

32.9

Rank #239 Confirmed

Summary

  • They share 12 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-4.5V leads 38.9 to 22.4.
  • Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • GLM-4.5V accepts more context: 64K tokens versus 33K.

Side by side

GLM-4.5VQwen2.5-Coder-32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.232.9
Released2025-08-112024-09-18
WeightsOpenOpen
Context window64K33K
Max output16K29K
Input $ / M tokens$0.60$0.66
Output $ / M tokens$1.80$1
Results tracked1531

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 38.9 (#152), Qwen2.5-Coder-32B: 22.4 (#328)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Coding13471276
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-4.5V leads

GLM-4.5V: 26.5 (#118), Qwen2.5-Coder-32B: 20.4 (#220)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Hard Prompts13351251
Kagi LLM Benchmark59.8%—
LiveBench Reasoning—42.1%
LiveBench Data Analysis—49.9%
Epoch Capabilities Index—119.41
HellaSwag—83%
LiveBench—46.2%
WinoGrande—80.8%

Math GLM-4.5V leads

GLM-4.5V: 34.7 (#157), Qwen2.5-Coder-32B: 30.8 (#203)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Math13531251
LiveBench Math—46.6%
GSM8K—93%

Knowledge GLM-4.5V leads

GLM-4.5V: 36.8 (#155), Qwen2.5-Coder-32B: 32.9 (#200)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Expert13531221
ARC (AI2) Challenge—70.5%
MMLU—79.1%

Multimodal Not comparable

GLM-4.5V: 33.6 (#86), Qwen2.5-Coder-32B: —

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Vision1154—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.9 (#172), Qwen2.5-Coder-32B: 38.0 (#230)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Non-English13041205
LMArena Chinese13381222
LMArena Russian12991228
LMArena Spanish1335—

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.4 (#170), Qwen2.5-Coder-32B: 61.6 (#240)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Instruction Following13111223
LiveBench Instruction Following—58.7%

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#166), Qwen2.5-Coder-32B: 37.9 (#204)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Longer Query13051251

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.7 (#162), Qwen2.5-Coder-32B: 41.8 (#235)

BenchmarkGLM-4.5VQwen2.5-Coder-32B
LMArena Text13331230
LMArena Creative Writing12951174
LMArena Multi-Turn13331222
LiveBench Language—23.3%

Frequently asked questions

Is GLM-4.5V better than Qwen2.5-Coder-32B?

GLM-4.5V is the stronger model overall, scoring 39.2 to 32.9 on the Noometry Index.

Which is cheaper, GLM-4.5V or Qwen2.5-Coder-32B?

Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Qwen2.5-Coder-32B better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 22.4 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5V does, with 64K tokens against 33K.

How many benchmarks do GLM-4.5V and Qwen2.5-Coder-32B share?

12 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen2.5-Coder-32B has 31.

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