Noometry

Model comparison

GLM-4.5V vs Qwen3.5 122B-A10B

Qwen3.5 122B-A10B is the stronger model overall, scoring 42.1 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 122B-A10B Alibaba (Qwen)

42.1

Rank #119 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 2 categories and Qwen3.5 122B-A10B in 7 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where Qwen3.5 122B-A10B leads 60.0 to 52.5.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.40 / $3.20 for Qwen3.5 122B-A10B.
  • Qwen3.5 122B-A10B accepts more context: 262K tokens versus 64K.

Side by side

GLM-4.5VQwen3.5 122B-A10B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.842.1
Released2025-08-112026-02-23
WeightsOpenOpen
Context window64K262K
Max output16K66K
Input $ / M tokens$0.60$0.40
Output $ / M tokens$1.80$3.20
Results tracked1527

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Category by category

Coding Too close to call

GLM-4.5V: 39.5 (#155), Qwen3.5 122B-A10B: 39.1 (#162)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Coding13471436
LMArena WebDev—1360
SciCode—35.6%

Reasoning Too close to call

GLM-4.5V: 27.4 (#119), Qwen3.5 122B-A10B: 27.2 (#123)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Hard Prompts13341421
Kagi LLM Benchmark59.8%—
NYT Connections (extended)—51.7%
CritPt—0.9%
Thematic Generalization—51.2%
Mystery Game Puzzles—17%
DTBench—84.3%
LMCA—32.2%

Math Qwen3.5 122B-A10B leads

GLM-4.5V: 37.4 (#159), Qwen3.5 122B-A10B: 39.1 (#112)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Math13541432

Knowledge Qwen3.5 122B-A10B leads

GLM-4.5V: 37.5 (#156), Qwen3.5 122B-A10B: 38.8 (#142)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Expert13531432
Vectara Hallucination Rate—11.2%

Multimodal Qwen3.5 122B-A10B leads

GLM-4.5V: 34.3 (#92), Qwen3.5 122B-A10B: 39.6 (#57)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Vision11541245

Multilingual Qwen3.5 122B-A10B leads

GLM-4.5V: 44.6 (#177), Qwen3.5 122B-A10B: 51.6 (#107)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Non-English13031400
LMArena Chinese13371462
LMArena Russian12981400
LMArena Spanish13361424
LMArena French—1442
LMArena German—1426
LMArena Japanese—1367
LMArena Korean—1352

Instruction Following Qwen3.5 122B-A10B leads

GLM-4.5V: 69.2 (#175), Qwen3.5 122B-A10B: 73.8 (#115)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Instruction Following13111399

Long Context Qwen3.5 122B-A10B leads

GLM-4.5V: 39.6 (#171), Qwen3.5 122B-A10B: 43.0 (#109)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Longer Query13041410

Writing & Preference Qwen3.5 122B-A10B leads

GLM-4.5V: 52.5 (#170), Qwen3.5 122B-A10B: 60.0 (#105)

BenchmarkGLM-4.5VQwen3.5 122B-A10B
LMArena Text13331417
LMArena Creative Writing12951368
LMArena Multi-Turn13321416

Frequently asked questions

Is GLM-4.5V better than Qwen3.5 122B-A10B?

Qwen3.5 122B-A10B is the stronger model overall, scoring 42.1 to 39.8 on the Noometry Index.

Which is cheaper, GLM-4.5V or Qwen3.5 122B-A10B?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Qwen3.5 122B-A10B lists at $0.40 and $3.20.

Is GLM-4.5V or Qwen3.5 122B-A10B better for coding?

They score almost the same on coding (39.5 vs 39.1); test both on your own repository before choosing.

Which has the bigger context window?

Qwen3.5 122B-A10B does, with 262K tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3.5 122B-A10B share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3.5 122B-A10B has 27.

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