Noometry

Model comparison

GLM-4.5V vs Qwen3.5 35B-A3B

Qwen3.5 35B-A3B is the stronger model overall, scoring 41.7 to 39.2 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.2

Rank #158 Confirmed

Qwen3.5 35B-A3B Alibaba (Qwen)

41.7

Rank #118 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 2 categories and Qwen3.5 35B-A3B in 6 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where Qwen3.5 35B-A3B leads 47.7 to 36.8.
  • Qwen3.5 35B-A3B is cheaper at $0.25 / $2 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • Qwen3.5 35B-A3B accepts more context: 262K tokens versus 64K.

Side by side

GLM-4.5VQwen3.5 35B-A3B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.241.7
Released2025-08-112026-02-01
WeightsOpenOpen
Context window64K262K
Max output16K66K
Input $ / M tokens$0.60$0.25
Output $ / M tokens$1.80$2
Results tracked1528

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 38.9 (#152), Qwen3.5 35B-A3B: 33.5 (#244)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Coding13471410
LMArena WebDev—1254
SciCode—29.3%

Reasoning GLM-4.5V leads

GLM-4.5V: 26.5 (#118), Qwen3.5 35B-A3B: 23.9 (#158)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Hard Prompts13351400
Kagi LLM Benchmark59.8%—
CritPt—0.6%
Chess Puzzles—10%
DTBench—80%
LMCA—29.5%
Epoch Capabilities Index—142.52

Math Qwen3.5 35B-A3B leads

GLM-4.5V: 34.7 (#157), Qwen3.5 35B-A3B: 38.4 (#86)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Math13531404
MathArena Final-Answer Competitions—56%
OTIS Mock AIME 2024-2025—70%

Knowledge Qwen3.5 35B-A3B leads

GLM-4.5V: 36.8 (#155), Qwen3.5 35B-A3B: 47.7 (#73)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Expert13531408
GPQA Diamond—83.5%
Vectara Hallucination Rate—10.5%

Multimodal Not comparable

GLM-4.5V: 33.6 (#86), Qwen3.5 35B-A3B: —

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Vision1154—

Multilingual Qwen3.5 35B-A3B leads

GLM-4.5V: 44.9 (#172), Qwen3.5 35B-A3B: 50.2 (#122)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Non-English13041379
LMArena Chinese13381458
LMArena Russian12991377
LMArena Spanish13351390
LMArena French—1412
LMArena German—1367
LMArena Japanese—1326
LMArena Korean—1354

Instruction Following Qwen3.5 35B-A3B leads

GLM-4.5V: 69.4 (#170), Qwen3.5 35B-A3B: 73.0 (#123)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Instruction Following13111379

Long Context Qwen3.5 35B-A3B leads

GLM-4.5V: 39.6 (#166), Qwen3.5 35B-A3B: 42.4 (#122)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Longer Query13051390

Writing & Preference Qwen3.5 35B-A3B leads

GLM-4.5V: 52.7 (#162), Qwen3.5 35B-A3B: 58.1 (#118)

BenchmarkGLM-4.5VQwen3.5 35B-A3B
LMArena Text13331395
LMArena Creative Writing12951347
LMArena Multi-Turn13331391

Frequently asked questions

Is GLM-4.5V better than Qwen3.5 35B-A3B?

Qwen3.5 35B-A3B is the stronger model overall, scoring 41.7 to 39.2 on the Noometry Index.

Which is cheaper, GLM-4.5V or Qwen3.5 35B-A3B?

Qwen3.5 35B-A3B is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Qwen3.5 35B-A3B better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 33.5 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5 35B-A3B does, with 262K tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3.5 35B-A3B share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3.5 35B-A3B has 28.

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