Noometry

Model comparison

GLM-5.3-Flash vs Qwen3 14B

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 35.5 on the Noometry Index.

Last verified . 6 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Qwen3 14B Alibaba (Qwen)

35.5

Rank #225 Confirmed

Summary

  • They share 6 benchmarks with published results for both. GLM-5.3-Flash scores higher in 6 categories and Qwen3 14B in 0 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 18.5.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 66.4% for Qwen3 14B.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 131K.

Side by side

GLM-5.3-FlashQwen3 14B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index51.835.5
Released2026-08-202025-04
WeightsOpenOpen
Context window1M131K
Max output131K8K
Input $ / M tokens$0.15$0.35
Output $ / M tokens$0.50$1.40
Results tracked4012

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Qwen3 14B: 37.3 (#195)

BenchmarkGLM-5.3-FlashQwen3 14B
SciCode51.6%31.6%
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
LMArena Coding1508—
ALE-Bench303.55—

Agentic & Tool Use GLM-5.3-Flash leads

GLM-5.3-Flash: 34.2 (#47), Qwen3 14B: 29.6 (#83)

BenchmarkGLM-5.3-FlashQwen3 14B
APEX-Agents52.8%—
Berkeley Function Calling Leaderboard—41%
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Qwen3 14B: 18.5 (#280)

BenchmarkGLM-5.3-FlashQwen3 14B
CritPt15.4%0%
Chess Puzzles14%4%
Epoch Capabilities Index151.88138.23
ARC-AGI-265.8%—
Kagi LLM Benchmark—49.1%
ARC-AGI-191%—
LMArena Hard Prompts1491—
Mystery Game Puzzles8%—
DTBench—64%
LMCA—18.2%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Qwen3 14B: 38.6 (#133)

BenchmarkGLM-5.3-FlashQwen3 14B
OTIS Mock AIME 2024-202593.9%66.4%
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
ProofBench21%—
LMArena Math1500—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Qwen3 14B: 39.3 (#134)

BenchmarkGLM-5.3-FlashQwen3 14B
GPQA Diamond90.2%63.8%
Vectara Hallucination Rate—5.4%
LMArena Expert1513—

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Qwen3 14B: —

BenchmarkGLM-5.3-FlashQwen3 14B
LMArena Vision1296—

Multilingual Not comparable

GLM-5.3-Flash: 56.0 (#25), Qwen3 14B: —

BenchmarkGLM-5.3-FlashQwen3 14B
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 14B: —

BenchmarkGLM-5.3-FlashQwen3 14B
LMArena Instruction Following1478—

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Qwen3 14B: 38.1 (#204)

BenchmarkGLM-5.3-FlashQwen3 14B
Fiction.LiveBench—62.5%
LMArena Longer Query1482—

Writing & Preference Not comparable

GLM-5.3-Flash: 65.3 (#50), Qwen3 14B: —

BenchmarkGLM-5.3-FlashQwen3 14B
LMArena Text1471—
LMArena Creative Writing1442—
LMArena Multi-Turn1467—

Frequently asked questions

Is GLM-5.3-Flash better than Qwen3 14B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 35.5 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or Qwen3 14B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.

Is GLM-5.3-Flash or Qwen3 14B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 37.3 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 131K.

How many benchmarks do GLM-5.3-Flash and Qwen3 14B share?

6 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3 14B has 12.

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