Noometry

Model comparison

GLM-5.3-Flash vs Qwen3.7 Flash

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 4.3× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

Last verified . 6 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Qwen3.7 Flash Alibaba (Qwen)

39.9

Rank #156 Confirmed

Summary

  • They share 6 benchmarks with published results for both. GLM-5.3-Flash scores higher in 3 categories and Qwen3.7 Flash in 0 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 28.2.
  • The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 55.8% for GLM-5.3-Flash and 19.3% for Qwen3.7 Flash.
  • Qwen3.7 Flash is cheaper at $0.03 / $0.13 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
  • GLM-5.3-Flash has downloadable open weights; the other is API-only.

Side by side

GLM-5.3-FlashQwen3.7 Flash
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index51.839.9
Released2026-08-202026-07-15
WeightsOpenProprietary
Context window1M1M
Max output131K131K
Input $ / M tokens$0.15$0.03
Output $ / M tokens$0.50$0.13
Results tracked407

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Category by category

Coding Not comparable

GLM-5.3-Flash: 53.1 (#31), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
SciCode51.6%—
LMArena Coding1508—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Qwen3.7 Flash: 28.2 (#108)

BenchmarkGLM-5.3-FlashQwen3.7 Flash
Chess Puzzles14%23%
Mystery Game Puzzles8%15%
Epoch Capabilities Index151.88144.64
ARC-AGI-265.8%—
NYT Connections (extended)—43.8%
ARC-AGI-191%—
CritPt15.4%—
LMArena Hard Prompts1491—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Qwen3.7 Flash: 38.3 (#140)

BenchmarkGLM-5.3-FlashQwen3.7 Flash
FrontierMath (Tiers 1-3)55.8%19.3%
OTIS Mock AIME 2024-202593.9%86.7%
FrontierMath Tier 417.1%—
ProofBench21%—
LMArena Math1500—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Qwen3.7 Flash: 48.9 (#75)

BenchmarkGLM-5.3-FlashQwen3.7 Flash
GPQA Diamond90.2%82.3%
LMArena Expert1513—

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
LMArena Vision1296—

Multilingual Not comparable

GLM-5.3-Flash: 56.0 (#25), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
LMArena Non-English1462—
LMArena Chinese1527—
LMArena French1496—
LMArena German1470—
LMArena Japanese1429—
LMArena Korean1446—
LMArena Russian1469—
LMArena Spanish1471—

Instruction Following Not comparable

GLM-5.3-Flash: 77.5 (#20), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
LMArena Instruction Following1478—

Long Context Not comparable

GLM-5.3-Flash: 45.4 (#39), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
LMArena Longer Query1482—

Writing & Preference Not comparable

GLM-5.3-Flash: 65.3 (#50), Qwen3.7 Flash: —

BenchmarkGLM-5.3-FlashQwen3.7 Flash
LMArena Text1471—
LMArena Creative Writing1442—
LMArena Multi-Turn1467—

Frequently asked questions

Is GLM-5.3-Flash better than Qwen3.7 Flash?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 4.3× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

Which is cheaper, GLM-5.3-Flash or Qwen3.7 Flash?

Qwen3.7 Flash is cheaper. It lists at $0.03 per million input tokens and $0.13 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.

Which has the bigger context window?

Both accept 1M tokens.

How many benchmarks do GLM-5.3-Flash and Qwen3.7 Flash share?

6 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.7 Flash has 7.

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