Noometry

Model comparison

GLM-5.3-Flash vs Qwen3 235B-A22B

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 43.2 on the Noometry Index.

Last verified . 24 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 Confirmed

Qwen3 235B-A22B Alibaba (Qwen)

43.2

Rank #83 Confirmed

Summary

  • They share 24 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen3 235B-A22B in 1 category; 7 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-5.3-Flash leads 31.7 to 15.8.
  • The biggest single-benchmark swing is CritPt: 15.4% for GLM-5.3-Flash and 0% for Qwen3 235B-A22B.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 131K.

Side by side

GLM-5.3-FlashQwen3 235B-A22B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index49.643.2
Released2026-08-202025-04
WeightsOpenOpen
Context window1M131K
Max output131K16K
Input $ / M tokens$0.15$0.70
Output $ / M tokens$0.50$2.80
Results tracked3649

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.7 (#26), Qwen3 235B-A22B: 43.8 (#72)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
SciCode51.6%42.4%
LMArena Coding15061445
DeepSWE63.4%—
FrontierCode31.8%—
Aider Polyglot—59.6%
LMArena WebDev1614—
WeirdML—41%
ALE-Bench303.55—

Agentic & Tool Use Too close to call

GLM-5.3-Flash: 34.3 (#41), Qwen3 235B-A22B: 33.7 (#48)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
APEX-Agents52.8%—
Berkeley Function Calling Leaderboard—52.1%
GDP.pdf14%—
Vending-Bench 2—-11.34

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 31.7 (#69), Qwen3 235B-A22B: 15.8 (#302)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
CritPt15.4%0%
Chess Puzzles14%12%
LMArena Hard Prompts14901433
Mystery Game Puzzles8%9%
Epoch Capabilities Index151.9143.87
ARC-AGI-2—1.3%
SimpleBench—31%
Kagi LLM Benchmark—69.4%
ARC-AGI-1—11%
DTBench—80.3%
LMCA—29.3%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
ForecastBench—59.7

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#38), Qwen3 235B-A22B: 48.5 (#51)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
OTIS Mock AIME 2024-202593.9%86.7%
LMArena Math14991431
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
ProofBench21%—
Omni-MATH—71.8%
MATH Level 5—68.9%
FrontierMath (Feb 2025 set)—8.5%
FrontierMath Tier 4 (v1)—0%

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#33), Qwen3 235B-A22B: 49.5 (#67)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
GPQA Diamond90.2%80.1%
LMArena Expert15121460
SimpleQA Verified—40.4%
MMLU-Pro—84.4%
Confabulations—15.6%
Vectara Hallucination Rate—9.3%
GPQA (HELM)—72.7%

Multimodal Not comparable

GLM-5.3-Flash: 42.6 (#25), Qwen3 235B-A22B: —

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
LMArena Vision1304—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.2 (#24), Qwen3 235B-A22B: 52.5 (#84)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
LMArena Non-English14621410
LMArena Chinese15291481
LMArena French14951445
LMArena German14571432
LMArena Japanese14201400
LMArena Korean14431391
LMArena Russian14721411
LMArena Spanish14711429

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.8 (#17), Qwen3 235B-A22B: 72.8 (#132)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
LMArena Instruction Following14801408
IFEval—83.5%

Long Context Too close to call

GLM-5.3-Flash: 45.3 (#41), Qwen3 235B-A22B: 46.1 (#25)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
LMArena Longer Query14791426
Fiction.LiveBench—75%

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.4 (#49), Qwen3 235B-A22B: 59.8 (#103)

BenchmarkGLM-5.3-FlashQwen3 235B-A22B
LMArena Text14711419
LMArena Creative Writing14401384
LMArena Multi-Turn14711432
Short-Story Creative Writing—83%
EQ-Bench Creative Writing—1366
WildBench—86.6%

Frequently asked questions

Is GLM-5.3-Flash better than Qwen3 235B-A22B?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 43.2 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or Qwen3 235B-A22B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.

Is GLM-5.3-Flash or Qwen3 235B-A22B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 43.8 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 235B-A22B share?

24 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen3 235B-A22B has 49.

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