Noometry

Model comparison

GLM-4.6 vs Qwen3.5 27B

GLM-4.6 and Qwen3.5 27B score almost the same on the Noometry Index (41.4 vs 41.9), so choose on price, context window or the category you care about most.

Last verified . 20 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3.5 27B Alibaba (Qwen)

41.9

Rank #127 Confirmed

Summary

  • They share 20 benchmarks with published results for both. GLM-4.6 scores higher in 7 categories and Qwen3.5 27B in 1 category; 5 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Qwen3.5 27B leads 27.5 to 23.7.
  • Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • Qwen3.5 27B accepts more context: 262K tokens versus 205K.

Side by side

GLM-4.6Qwen3.5 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.441.9
Released2025-09-302026-02-23
WeightsOpenOpen
Context window205K262K
Max output131K66K
Input $ / M tokens$0.60$0.30
Output $ / M tokens$2.20$2.40
Results tracked2928

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Qwen3.5 27B: 38.9 (#168)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena WebDev13401358
LMArena Coding14491427
ALE-Bench340.82349.45
SWE-bench Verified (bash only)55.4%—
SciCode38.4%—
WeirdML—39.5%

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Qwen3.5 27B: —

BenchmarkGLM-4.6Qwen3.5 27B
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—
Vending-Bench 2—201.98

Reasoning Qwen3.5 27B leads

GLM-4.6: 23.7 (#172), Qwen3.5 27B: 27.5 (#117)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Hard Prompts14401414
Kagi LLM Benchmark47.4%—
NYT Connections (extended)—47.9%
CritPt1.1%—
Thematic Generalization—45.5%
DTBench—82.4%
LMCA—34%

Math Too close to call

GLM-4.6: 39.1 (#111), Qwen3.5 27B: 38.8 (#127)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Math14321429
MathArena Final-Answer Competitions—56.7%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Qwen3.5 27B: 38.0 (#150)

BenchmarkGLM-4.6Qwen3.5 27B
Vectara Hallucination Rate9.5%12.1%
LMArena Expert14311428

Multimodal Not comparable

GLM-4.6: —, Qwen3.5 27B: 39.4 (#59)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Vision—1241

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Qwen3.5 27B: 50.8 (#115)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Non-English14261390
LMArena Chinese14991478
LMArena French14591410
LMArena German14471393
LMArena Japanese13931345
LMArena Korean14001358
LMArena Russian14191390
LMArena Spanish14361407

Instruction Following Too close to call

GLM-4.6: 74.3 (#98), Qwen3.5 27B: 73.5 (#119)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Instruction Following14101393

Long Context Too close to call

GLM-4.6: 43.4 (#94), Qwen3.5 27B: 43.1 (#106)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Longer Query14221413

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Qwen3.5 27B: 59.3 (#111)

BenchmarkGLM-4.6Qwen3.5 27B
LMArena Text14401409
LMArena Creative Writing14111362
LMArena Multi-Turn14271410
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than Qwen3.5 27B?

GLM-4.6 and Qwen3.5 27B score almost the same on the Noometry Index (41.4 vs 41.9), so choose on price, context window or the category you care about most.

Which is cheaper, GLM-4.6 or Qwen3.5 27B?

Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or Qwen3.5 27B better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 38.9 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5 27B does, with 262K tokens against 205K.

How many benchmarks do GLM-4.6 and Qwen3.5 27B share?

20 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3.5 27B has 28.

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