Noometry

Model comparison

GLM-4.5V vs Qwen3 32B

GLM-4.5V and Qwen3 32B score almost the same on the Noometry Index (39.8 vs 39.2), so choose on price, context window or the category you care about most.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Qwen3 32B Alibaba (Qwen)

39.2

Rank #172 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 3 categories and Qwen3 32B in 5 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-4.5V leads 27.4 to 20.2.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
  • Qwen3 32B accepts more context: 131K tokens versus 64K.

Side by side

GLM-4.5VQwen3 32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.839.2
Released2025-08-112025-04
WeightsOpenOpen
Context window64K131K
Max output16K16K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$1.80$2.80
Results tracked1526

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Qwen3 32B: 37.7 (#190)

BenchmarkGLM-4.5VQwen3 32B
LMArena Coding13471358
Aider Polyglot—40%
SciCode—35.4%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Qwen3 32B: 32.6 (#62)

BenchmarkGLM-4.5VQwen3 32B
Berkeley Function Calling Leaderboard—48.7%

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), Qwen3 32B: 20.2 (#241)

BenchmarkGLM-4.5VQwen3 32B
Kagi LLM Benchmark59.8%54.9%
LMArena Hard Prompts13341334
CritPt—0.3%
Chess Puzzles—5%
DTBench—67.5%
LMCA—17.3%
Epoch Capabilities Index—138.51

Math Qwen3 32B leads

GLM-4.5V: 37.4 (#159), Qwen3 32B: 39.7 (#99)

BenchmarkGLM-4.5VQwen3 32B
LMArena Math13541399
OTIS Mock AIME 2024-2025—66.9%

Knowledge Qwen3 32B leads

GLM-4.5V: 37.5 (#156), Qwen3 32B: 40.0 (#125)

BenchmarkGLM-4.5VQwen3 32B
LMArena Expert13531362
GPQA Diamond—65.7%
Vectara Hallucination Rate—5.9%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Qwen3 32B: —

BenchmarkGLM-4.5VQwen3 32B
LMArena Vision1154—

Multilingual Too close to call

GLM-4.5V: 44.6 (#177), Qwen3 32B: 45.6 (#167)

BenchmarkGLM-4.5VQwen3 32B
LMArena Non-English13031317
LMArena Chinese13371357
LMArena Russian12981311
LMArena German—1341
LMArena Spanish1336—

Instruction Following Too close to call

GLM-4.5V: 69.2 (#175), Qwen3 32B: 68.9 (#179)

BenchmarkGLM-4.5VQwen3 32B
LMArena Instruction Following13111305

Long Context Qwen3 32B leads

GLM-4.5V: 39.6 (#171), Qwen3 32B: 43.8 (#87)

BenchmarkGLM-4.5VQwen3 32B
LMArena Longer Query13041327
Fiction.LiveBench—74.2%

Writing & Preference Too close to call

GLM-4.5V: 52.5 (#170), Qwen3 32B: 52.9 (#163)

BenchmarkGLM-4.5VQwen3 32B
LMArena Text13331340
LMArena Creative Writing12951297
LMArena Multi-Turn13321331

Frequently asked questions

Is GLM-4.5V better than Qwen3 32B?

GLM-4.5V and Qwen3 32B score almost the same on the Noometry Index (39.8 vs 39.2), so choose on price, context window or the category you care about most.

Which is cheaper, GLM-4.5V or Qwen3 32B?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

Is GLM-4.5V or Qwen3 32B better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 37.7 in the Noometry coding category.

Which has the bigger context window?

Qwen3 32B does, with 131K tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3 32B share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3 32B has 26.

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