Noometry

Model comparison

GLM-5.3-Flash vs Qwen1.5-110B

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 34.2 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Qwen1.5-110B Alibaba (Qwen)

34.2

Rank #234 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen1.5-110B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-5.3-Flash leads 65.3 to 38.0.

Side by side

GLM-5.3-FlashQwen1.5-110B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index51.834.2
Released2026-08-202024-04-25
WeightsOpenOpen
Context window1M—
Max output131K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.50—
Results tracked4020

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Qwen1.5-110B: 33.0 (#264)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Coding15081184
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
SciCode51.6%—
BigCodeBench Instruct—35%
BigCodeBench Complete—44.4%
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Qwen1.5-110B: —

BenchmarkGLM-5.3-FlashQwen1.5-110B
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Qwen1.5-110B: 22.7 (#189)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Hard Prompts14911168
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—
ForecastBench—57.7

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Qwen1.5-110B: 33.7 (#201)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Math15001185
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), Qwen1.5-110B: 31.2 (#219)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Expert15131144
GPQA Diamond90.2%—

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Qwen1.5-110B: —

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Vision1296—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Qwen1.5-110B: 33.6 (#250)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Non-English14621142
LMArena Chinese15271206
LMArena French14961151
LMArena German14701123
LMArena Japanese14291074
LMArena Korean14461044
LMArena Russian14691118
LMArena Spanish14711142

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Qwen1.5-110B: 60.3 (#252)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Instruction Following14781158

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Qwen1.5-110B: 35.1 (#242)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Longer Query14821157

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Qwen1.5-110B: 38.0 (#255)

BenchmarkGLM-5.3-FlashQwen1.5-110B
LMArena Text14711175
LMArena Creative Writing14421148
LMArena Multi-Turn14671160

Frequently asked questions

Is GLM-5.3-Flash better than Qwen1.5-110B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 34.2 on the Noometry Index.

Is GLM-5.3-Flash or Qwen1.5-110B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 33.0 in the Noometry coding category.

How many benchmarks do GLM-5.3-Flash and Qwen1.5-110B share?

17 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen1.5-110B has 20.

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