Noometry

Model comparison

GLM-4.6V vs Qwen3.5 27B

GLM-4.6V and Qwen3.5 27B score almost the same on the Noometry Index (40.9 vs 41.4), so choose on price, context window or the category you care about most.

Last verified . 12 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Qwen3.5 27B Alibaba (Qwen)

41.4

Rank #127 Confirmed

Summary

  • They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 2 categories and Qwen3.5 27B in 6 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in multimodal, where Qwen3.5 27B leads 38.7 to 34.2.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.30 / $2.40 for Qwen3.5 27B.
  • Qwen3.5 27B accepts more context: 262K tokens versus 128K.

Side by side

GLM-4.6VQwen3.5 27B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index40.941.4
Released2025-12-082026-02-23
WeightsOpenOpen
Context window128K262K
Max output33K66K
Input $ / M tokens$0.30$0.30
Output $ / M tokens$0.90$2.40
Results tracked1228

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Qwen3.5 27B: 38.6 (#159)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Coding13901427
LMArena WebDev—1357
WeirdML—39.5%
ALE-Bench—349.45

Agentic & Tool Use Not comparable

GLM-4.6V: —, Qwen3.5 27B: —

BenchmarkGLM-4.6VQwen3.5 27B
Vending-Bench 2—201.98

Reasoning Too close to call

GLM-4.6V: 26.5 (#117), Qwen3.5 27B: 26.6 (#116)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Hard Prompts13681414
NYT Connections (extended)—47.9%
Thematic Generalization—45.5%
DTBench—82.4%
LMCA—34%

Math Not comparable

GLM-4.6V: —, Qwen3.5 27B: 36.8 (#103)

BenchmarkGLM-4.6VQwen3.5 27B
MathArena Final-Answer Competitions—56.7%
LMArena Math—1428

Knowledge Too close to call

GLM-4.6V: 37.4 (#148), Qwen3.5 27B: 37.4 (#149)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Expert13721428
Vectara Hallucination Rate—12.1%

Multimodal Qwen3.5 27B leads

GLM-4.6V: 34.2 (#83), Qwen3.5 27B: 38.7 (#56)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Vision11641240

Multilingual Qwen3.5 27B leads

GLM-4.6V: 48.8 (#135), Qwen3.5 27B: 51.0 (#112)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Non-English13591390
LMArena Chinese14261478
LMArena Russian13411389
LMArena French—1409
LMArena German—1393
LMArena Japanese—1345
LMArena Korean—1359
LMArena Spanish—1408

Instruction Following Qwen3.5 27B leads

GLM-4.6V: 71.6 (#145), Qwen3.5 27B: 73.7 (#114)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Instruction Following13511393

Long Context Qwen3.5 27B leads

GLM-4.6V: 41.3 (#138), Qwen3.5 27B: 43.1 (#103)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Longer Query13581413

Writing & Preference Qwen3.5 27B leads

GLM-4.6V: 56.8 (#132), Qwen3.5 27B: 59.5 (#106)

BenchmarkGLM-4.6VQwen3.5 27B
LMArena Text13771409
LMArena Creative Writing13471362
LMArena Multi-Turn13591410

Frequently asked questions

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

GLM-4.6V and Qwen3.5 27B score almost the same on the Noometry Index (40.9 vs 41.4), so choose on price, context window or the category you care about most.

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

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.

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

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 38.6 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5 27B does, with 262K tokens against 128K.

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

12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3.5 27B has 28.

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