Noometry

Model comparison

GLM-5.3-Flash vs Qwen3.6 27B

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 41.9 on the Noometry Index.

Last verified . 8 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 Confirmed

Qwen3.6 27B Alibaba (Qwen)

41.9

Rank #112 Confirmed

Summary

  • They share 8 benchmarks with published results for both. GLM-5.3-Flash scores higher in 5 categories and Qwen3.6 27B in 0 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-5.3-Flash leads 53.7 to 38.5.
  • The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 55.8% for GLM-5.3-Flash and 35.1% for Qwen3.6 27B.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.60 / $3.60 for Qwen3.6 27B.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 262K.

Side by side

GLM-5.3-FlashQwen3.6 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index49.641.9
Released2026-08-202026-04-22
WeightsOpenOpen
Context window1M262K
Max output131K66K
Input $ / M tokens$0.15$0.60
Output $ / M tokens$0.50$3.60
Results tracked3611

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.7 (#26), Qwen3.6 27B: 38.5 (#162)

BenchmarkGLM-5.3-FlashQwen3.6 27B
SciCode51.6%37.3%
DeepSWE63.4%—
FrontierCode31.8%—
LMArena WebDev1614—
LMArena Coding1506—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.3 (#41), Qwen3.6 27B: —

BenchmarkGLM-5.3-FlashQwen3.6 27B
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 31.7 (#69), Qwen3.6 27B: 24.4 (#148)

BenchmarkGLM-5.3-FlashQwen3.6 27B
CritPt15.4%0.9%
Chess Puzzles14%22%
Mystery Game Puzzles8%7%
Epoch Capabilities Index151.9146.47
LMArena Hard Prompts1490—
DTBench—78.1%
LMCA—34.5%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#38), Qwen3.6 27B: 47.5 (#56)

BenchmarkGLM-5.3-FlashQwen3.6 27B
FrontierMath (Tiers 1-3)55.8%35.1%
OTIS Mock AIME 2024-202593.9%91.1%
FrontierMath Tier 417.1%—
ProofBench21%—
LMArena Math1499—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#33), Qwen3.6 27B: 52.4 (#58)

BenchmarkGLM-5.3-FlashQwen3.6 27B
GPQA Diamond90.2%85.9%
LMArena Expert1512—

Multimodal Not comparable

GLM-5.3-Flash: 42.6 (#25), Qwen3.6 27B: —

BenchmarkGLM-5.3-FlashQwen3.6 27B
LMArena Vision1304—

Multilingual Not comparable

GLM-5.3-Flash: 56.2 (#24), Qwen3.6 27B: —

BenchmarkGLM-5.3-FlashQwen3.6 27B
LMArena Non-English1462—
LMArena Chinese1529—
LMArena French1495—
LMArena German1457—
LMArena Japanese1420—
LMArena Korean1443—
LMArena Russian1472—
LMArena Spanish1471—

Instruction Following Not comparable

GLM-5.3-Flash: 77.8 (#17), Qwen3.6 27B: —

BenchmarkGLM-5.3-FlashQwen3.6 27B
LMArena Instruction Following1480—

Long Context Not comparable

GLM-5.3-Flash: 45.3 (#41), Qwen3.6 27B: —

BenchmarkGLM-5.3-FlashQwen3.6 27B
LMArena Longer Query1479—

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.4 (#49), Qwen3.6 27B: 50.3 (#180)

BenchmarkGLM-5.3-FlashQwen3.6 27B
LMArena Text1471—
LMArena Creative Writing1440—
EQ-Bench 4—1026
LMArena Multi-Turn1471—

Frequently asked questions

Is GLM-5.3-Flash better than Qwen3.6 27B?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 41.9 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or Qwen3.6 27B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.6 27B lists at $0.60 and $3.60.

Is GLM-5.3-Flash or Qwen3.6 27B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 38.5 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 Qwen3.6 27B share?

8 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen3.6 27B has 11.

Related comparisons

Go deeper