Noometry

Model comparison

GLM-5.3-Flash vs Qwen1.5-72B

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.3 on the Noometry Index.

Last verified . 18 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 Confirmed

Qwen1.5-72B Alibaba (Qwen)

30.3

Rank #282 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen1.5-72B 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 11.5.
  • The biggest single-benchmark swing is GPQA Diamond: 90.2% for GLM-5.3-Flash and 28.8% for Qwen1.5-72B.

Side by side

GLM-5.3-FlashQwen1.5-72B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index49.630.3
Released2026-08-202024-02-04
WeightsOpenOpen
Context window1M—
Max output131K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.50—
Results tracked3622

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.7 (#26), Qwen1.5-72B: 31.4 (#272)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Coding15061166
DeepSWE63.4%—
FrontierCode31.8%—
LMArena WebDev1614—
SciCode51.6%—
BigCodeBench Instruct—33.2%
BigCodeBench Complete—40.3%
ALE-Bench303.55—
HumanEval+—59.1%
MBPP+—61.6%

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.3 (#41), Qwen1.5-72B: —

BenchmarkGLM-5.3-FlashQwen1.5-72B
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 31.7 (#69), Qwen1.5-72B: 21.2 (#201)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Hard Prompts14901148
CritPt15.4%—
Chess Puzzles14%—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
Epoch Capabilities Index151.9—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#38), Qwen1.5-72B: 30.7 (#205)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Math14991164
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 (#33), Qwen1.5-72B: 11.5 (#295)

BenchmarkGLM-5.3-FlashQwen1.5-72B
GPQA Diamond90.2%28.8%
LMArena Expert15121136

Multimodal Not comparable

GLM-5.3-Flash: 42.6 (#25), Qwen1.5-72B: —

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Vision1304—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.2 (#24), Qwen1.5-72B: 33.4 (#248)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Non-English14621135
LMArena Chinese15291186
LMArena French14951158
LMArena German14571084
LMArena Japanese14201061
LMArena Korean14431049
LMArena Russian14721104
LMArena Spanish14711110

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.8 (#17), Qwen1.5-72B: 59.6 (#251)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Instruction Following14801142

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.3 (#41), Qwen1.5-72B: 35.0 (#238)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Longer Query14791157

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.4 (#49), Qwen1.5-72B: 37.4 (#253)

BenchmarkGLM-5.3-FlashQwen1.5-72B
LMArena Text14711166
LMArena Creative Writing14401137
LMArena Multi-Turn14711160

Frequently asked questions

Is GLM-5.3-Flash better than Qwen1.5-72B?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.3 on the Noometry Index.

Is GLM-5.3-Flash or Qwen1.5-72B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 31.4 in the Noometry coding category.

How many benchmarks do GLM-5.3-Flash and Qwen1.5-72B share?

18 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen1.5-72B has 22.

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