Noometry

Model comparison

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

GLM-4.5 is the stronger model overall, scoring 41.4 to 32.9 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

41.4

Rank #125 Confirmed

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

32.9

Rank #239 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5 scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-4.5 leads 41.1 to 22.4.
  • The biggest single-benchmark swing is SWE-bench Verified (bash only): 54.2% for GLM-4.5 and 9% for Qwen2.5-Coder-32B.
  • Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
  • GLM-4.5 accepts more context: 131K tokens versus 33K.

Side by side

GLM-4.5Qwen2.5-Coder-32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.432.9
Released2025-07-272024-09-18
WeightsOpenOpen
Context window131K33K
Max output98K29K
Input $ / M tokens$0.60$0.66
Output $ / M tokens$2.20$1
Results tracked2731

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.1 (#121), Qwen2.5-Coder-32B: 22.4 (#328)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
SWE-bench Verified (bash only)54.2%9%
LMArena Coding14341276
Aider Polyglot—16.4%
WeirdML40.6%—
BigCodeBench Instruct—49%
LiveBench Coding—56.9%
BigCodeBench Complete—58%
ALE-Bench344.82—
AlgoTune1.52—
HumanEval+—87.2%
MBPP+—77%

Reasoning GLM-4.5 leads

GLM-4.5: 27.7 (#99), Qwen2.5-Coder-32B: 20.4 (#220)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Hard Prompts14301251
Kagi LLM Benchmark57.9%—
LiveBench Reasoning—42.1%
LiveBench Data Analysis—49.9%
Epoch Capabilities Index—119.41
HellaSwag—83%
LiveBench—46.2%
WinoGrande—80.8%

Math GLM-4.5 leads

GLM-4.5: 36.3 (#119), Qwen2.5-Coder-32B: 30.8 (#203)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Math14271251
LiveBench Math—46.6%
GSM8K—93%

Knowledge GLM-4.5 leads

GLM-4.5: 35.5 (#172), Qwen2.5-Coder-32B: 32.9 (#200)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Expert14331221
Humanity's Last Exam8.3%—
Confabulations11.3%—
ARC (AI2) Challenge—70.5%
MMLU—79.1%

Multilingual GLM-4.5 leads

GLM-4.5: 53.0 (#74), Qwen2.5-Coder-32B: 38.0 (#230)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Non-English14171205
LMArena Chinese14651222
LMArena Russian14141228
LMArena French1423—
LMArena German1406—
LMArena Japanese1416—
LMArena Korean1382—
LMArena Spanish1454—

Instruction Following GLM-4.5 leads

GLM-4.5: 74.3 (#100), Qwen2.5-Coder-32B: 61.6 (#240)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Instruction Following14051223
LiveBench Instruction Following—58.7%

Long Context Too close to call

GLM-4.5: 38.2 (#196), Qwen2.5-Coder-32B: 37.9 (#204)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Longer Query14131251
Fiction.LiveBench58.3%—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.7 (#121), Qwen2.5-Coder-32B: 41.8 (#235)

BenchmarkGLM-4.5Qwen2.5-Coder-32B
LMArena Text14301230
LMArena Creative Writing13951174
LMArena Multi-Turn14151222
Short-Story Creative Writing73.4%—
EQ-Bench Creative Writing1343—
LiveBench Language—23.3%

Frequently asked questions

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

GLM-4.5 is the stronger model overall, scoring 41.4 to 32.9 on the Noometry Index.

Which is cheaper, GLM-4.5 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-4.5 lists at $0.60 and $2.20.

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

GLM-4.5 scores higher on coding benchmarks: 41.1 versus 22.4 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5 does, with 131K tokens against 33K.

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

13 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen2.5-Coder-32B has 31.

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