Noometry

Model comparison

GLM-5.3-Flash vs Qwen3.5 122B-A10B

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.1 on the Noometry Index.

Last verified . 22 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Qwen3.5 122B-A10B Alibaba (Qwen)

42.1

Rank #119 Confirmed

Summary

  • They share 22 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Qwen3.5 122B-A10B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 27.2.
  • The biggest single-benchmark swing is SciCode: 51.6% for GLM-5.3-Flash and 35.6% for Qwen3.5 122B-A10B.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.40 / $3.20 for Qwen3.5 122B-A10B.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 262K.

Side by side

GLM-5.3-FlashQwen3.5 122B-A10B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index51.842.1
Released2026-08-202026-02-23
WeightsOpenOpen
Context window1M262K
Max output131K66K
Input $ / M tokens$0.15$0.40
Output $ / M tokens$0.50$3.20
Results tracked4027

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Qwen3.5 122B-A10B: 39.1 (#162)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena WebDev16091360
SciCode51.6%35.6%
LMArena Coding15081436
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
FrontierSWE18.1%—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Qwen3.5 122B-A10B: —

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Qwen3.5 122B-A10B: 27.2 (#123)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
CritPt15.4%0.9%
LMArena Hard Prompts14911421
Mystery Game Puzzles8%17%
ARC-AGI-265.8%—
NYT Connections (extended)—51.7%
ARC-AGI-191%—
Chess Puzzles14%—
Thematic Generalization—51.2%
DTBench—84.3%
LMCA—32.2%
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), Qwen3.5 122B-A10B: 39.1 (#112)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Math15001432
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), Qwen3.5 122B-A10B: 38.8 (#142)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Expert15131432
GPQA Diamond90.2%—
Vectara Hallucination Rate—11.2%

Multimodal GLM-5.3-Flash leads

GLM-5.3-Flash: 42.8 (#27), Qwen3.5 122B-A10B: 39.6 (#57)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Vision12961245

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Qwen3.5 122B-A10B: 51.6 (#107)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Non-English14621400
LMArena Chinese15271462
LMArena French14961442
LMArena German14701426
LMArena Japanese14291367
LMArena Korean14461352
LMArena Russian14691400
LMArena Spanish14711424

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Qwen3.5 122B-A10B: 73.8 (#115)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Instruction Following14781399

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Qwen3.5 122B-A10B: 43.0 (#109)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Longer Query14821410

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Qwen3.5 122B-A10B: 60.0 (#105)

BenchmarkGLM-5.3-FlashQwen3.5 122B-A10B
LMArena Text14711417
LMArena Creative Writing14421368
LMArena Multi-Turn14671416

Frequently asked questions

Is GLM-5.3-Flash better than Qwen3.5 122B-A10B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.1 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or Qwen3.5 122B-A10B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.5 122B-A10B lists at $0.40 and $3.20.

Is GLM-5.3-Flash or Qwen3.5 122B-A10B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 39.1 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 262K.

How many benchmarks do GLM-5.3-Flash and Qwen3.5 122B-A10B share?

22 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.5 122B-A10B has 27.

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