Noometry

Model comparison

GLM-4.5V vs Qwen3.5 27B

Qwen3.5 27B is the stronger model overall, scoring 41.9 to 39.8 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Qwen3.5 27B Alibaba (Qwen)

41.9

Rank #127 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 1 category and Qwen3.5 27B in 8 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where Qwen3.5 27B leads 59.3 to 52.5.
  • Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • Qwen3.5 27B accepts more context: 262K tokens versus 64K.

Side by side

GLM-4.5VQwen3.5 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.841.9
Released2025-08-112026-02-23
WeightsOpenOpen
Context window64K262K
Max output16K66K
Input $ / M tokens$0.60$0.30
Output $ / M tokens$1.80$2.40
Results tracked1528

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Category by category

Coding Too close to call

GLM-4.5V: 39.5 (#155), Qwen3.5 27B: 38.9 (#168)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Coding13471427
LMArena WebDev—1358
WeirdML—39.5%
ALE-Bench—349.45

Agentic & Tool Use Not comparable

GLM-4.5V: —, Qwen3.5 27B: —

BenchmarkGLM-4.5VQwen3.5 27B
Vending-Bench 2—201.98

Reasoning Too close to call

GLM-4.5V: 27.4 (#119), Qwen3.5 27B: 27.5 (#117)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Hard Prompts13341414
Kagi LLM Benchmark59.8%—
NYT Connections (extended)—47.9%
Thematic Generalization—45.5%
DTBench—82.4%
LMCA—34%

Math Qwen3.5 27B leads

GLM-4.5V: 37.4 (#159), Qwen3.5 27B: 38.8 (#127)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Math13541429
MathArena Final-Answer Competitions—56.7%

Knowledge Too close to call

GLM-4.5V: 37.5 (#156), Qwen3.5 27B: 38.0 (#150)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Expert13531428
Vectara Hallucination Rate—12.1%

Multimodal Qwen3.5 27B leads

GLM-4.5V: 34.3 (#92), Qwen3.5 27B: 39.4 (#59)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Vision11541241

Multilingual Qwen3.5 27B leads

GLM-4.5V: 44.6 (#177), Qwen3.5 27B: 50.8 (#115)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Non-English13031390
LMArena Chinese13371478
LMArena Russian12981390
LMArena Spanish13361407
LMArena French—1410
LMArena German—1393
LMArena Japanese—1345
LMArena Korean—1358

Instruction Following Qwen3.5 27B leads

GLM-4.5V: 69.2 (#175), Qwen3.5 27B: 73.5 (#119)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Instruction Following13111393

Long Context Qwen3.5 27B leads

GLM-4.5V: 39.6 (#171), Qwen3.5 27B: 43.1 (#106)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Longer Query13041413

Writing & Preference Qwen3.5 27B leads

GLM-4.5V: 52.5 (#170), Qwen3.5 27B: 59.3 (#111)

BenchmarkGLM-4.5VQwen3.5 27B
LMArena Text13331409
LMArena Creative Writing12951362
LMArena Multi-Turn13321410

Frequently asked questions

Is GLM-4.5V better than Qwen3.5 27B?

Qwen3.5 27B is the stronger model overall, scoring 41.9 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Qwen3.5 27B better for coding?

They score almost the same on coding (39.5 vs 38.9); test both on your own repository before choosing.

Which has the bigger context window?

Qwen3.5 27B does, with 262K tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3.5 27B share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3.5 27B has 28.

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