Noometry

Model comparison

GLM-5.3-Flash vs Qwen3.5 397B-A17B

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 46.0 on the Noometry Index.

Last verified . 26 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Qwen3.5 397B-A17B Alibaba (Qwen)

46.0

Rank #67 Confirmed

Summary

  • They share 26 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and Qwen3.5 397B-A17B 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 34.5.
  • The biggest single-benchmark swing is APEX-Agents: 52.8% for GLM-5.3-Flash and 24.9% for Qwen3.5 397B-A17B.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.60 / $3.60 for Qwen3.5 397B-A17B.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 262K.

Side by side

GLM-5.3-FlashQwen3.5 397B-A17B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index51.846.0
Released2026-08-202026-02-01
WeightsOpenOpen
Context window1M262K
Max output131K66K
Input $ / M tokens$0.15$0.60
Output $ / M tokens$0.50$3.60
Results tracked4036

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Qwen3.5 397B-A17B: 42.0 (#114)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
LMArena WebDev16091400
LMArena Coding15081465
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
FrontierSWE18.1%—
SciCode51.6%—
ALE-Bench303.55—

Agentic & Tool Use Too close to call

GLM-5.3-Flash: 34.2 (#47), Qwen3.5 397B-A17B: 33.3 (#53)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
APEX-Agents52.8%24.9%
τ²-bench Airline—81.5%
τ²-bench Banking—9.8%
τ²-bench Retail—84.4%
τ²-bench Telecom—97.8%
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Qwen3.5 397B-A17B: 34.5 (#70)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
Chess Puzzles14%13%
LMArena Hard Prompts14911448
Mystery Game Puzzles8%18%
Epoch Capabilities Index151.88146.65
ARC-AGI-265.8%—
Kagi LLM Benchmark—73.7%
NYT Connections (extended)—58.9%
ARC-AGI-191%—
CritPt15.4%—
Thematic Generalization—65.1%
DTBench—87.5%
LMCA—37.9%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Qwen3.5 397B-A17B: 46.1 (#73)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
FrontierMath (Tiers 1-3)55.8%31.2%
OTIS Mock AIME 2024-202593.9%88.9%
LMArena Math15001454
FrontierMath Tier 417.1%—
ProofBench21%—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Qwen3.5 397B-A17B: 53.3 (#58)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
GPQA Diamond90.2%86.4%
LMArena Expert15131462

Multimodal GLM-5.3-Flash leads

GLM-5.3-Flash: 42.8 (#27), Qwen3.5 397B-A17B: 40.7 (#44)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
LMArena Vision12961263

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Qwen3.5 397B-A17B: 53.7 (#59)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
LMArena Non-English14621430
LMArena Chinese15271500
LMArena French14961461
LMArena German14701447
LMArena Japanese14291426
LMArena Korean14461384
LMArena Russian14691429
LMArena Spanish14711441

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Qwen3.5 397B-A17B: 75.0 (#77)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
LMArena Instruction Following14781424

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Qwen3.5 397B-A17B: 44.1 (#74)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
LMArena Longer Query14821442

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Qwen3.5 397B-A17B: 62.3 (#79)

BenchmarkGLM-5.3-FlashQwen3.5 397B-A17B
LMArena Text14711438
LMArena Creative Writing14421401
LMArena Multi-Turn14671446
EQ-Bench Creative Writing—1478

Frequently asked questions

Is GLM-5.3-Flash better than Qwen3.5 397B-A17B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 46.0 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or Qwen3.5 397B-A17B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.5 397B-A17B lists at $0.60 and $3.60.

Is GLM-5.3-Flash or Qwen3.5 397B-A17B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 42.0 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 397B-A17B share?

26 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.5 397B-A17B has 36.

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