Noometry

Model comparison

GLM-5.3-Flash vs Qwen1.5-32B

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 30.5 on the Noometry Index.

Last verified . 18 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Qwen1.5-32B Alibaba (Qwen)

30.5

Rank #293 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen1.5-32B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 13.5.
  • The biggest single-benchmark swing is GPQA Diamond: 90.2% for GLM-5.3-Flash and 30.7% for Qwen1.5-32B.

Side by side

GLM-5.3-FlashQwen1.5-32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index51.830.5
Released2026-08-202024-02-04
WeightsOpenOpen
Context window1M—
Max output131K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.50—
Results tracked4021

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Qwen1.5-32B: 31.7 (#282)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Coding15081155
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
SciCode51.6%—
BigCodeBench Instruct—32.3%
BigCodeBench Complete—42%
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Qwen1.5-32B: —

BenchmarkGLM-5.3-FlashQwen1.5-32B
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Qwen1.5-32B: 21.8 (#212)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Hard Prompts14911130
ARC-AGI-265.8%—
ARC-AGI-191%—
CritPt15.4%—
Chess Puzzles14%—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
Epoch Capabilities Index151.88—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Qwen1.5-32B: 33.0 (#207)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Math15001155
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Qwen1.5-32B: 13.5 (#296)

BenchmarkGLM-5.3-FlashQwen1.5-32B
GPQA Diamond90.2%30.7%
LMArena Expert15131126
MMLU—74.4%

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Qwen1.5-32B: —

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Vision1296—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Qwen1.5-32B: 31.4 (#259)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Non-English14621106
LMArena Chinese15271177
LMArena French14961101
LMArena German14701058
LMArena Japanese14291027
LMArena Korean14461008
LMArena Russian14691073
LMArena Spanish14711089

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Qwen1.5-32B: 57.7 (#265)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Instruction Following14781116

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Qwen1.5-32B: 34.7 (#246)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Longer Query14821146

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Qwen1.5-32B: 34.2 (#271)

BenchmarkGLM-5.3-FlashQwen1.5-32B
LMArena Text14711137
LMArena Creative Writing14421083
LMArena Multi-Turn14671140

Frequently asked questions

Is GLM-5.3-Flash better than Qwen1.5-32B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 30.5 on the Noometry Index.

Is GLM-5.3-Flash or Qwen1.5-32B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 31.7 in the Noometry coding category.

How many benchmarks do GLM-5.3-Flash and Qwen1.5-32B share?

18 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen1.5-32B has 21.

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