Model comparison

GLM-4.6V vs Qwen3 32B

GLM-4.6V is the stronger model overall, scoring 41.3 to 39.2 on the Noometry Index.

Last verified October 10, 2026. 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

Qwen3 32B

Alibaba (Qwen)

39.2

Rank #172 Confirmed

Summary

They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 5 categories and Qwen3 32B in 2 categories; 7 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 20.2.
GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
Qwen3 32B accepts more context: 131K tokens versus 128K.

Side by side

GLM-4.6V and Qwen3 32B specifications
	GLM-4.6V	Qwen3 32B
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	41.3	39.2
Released	2025-12-08	2025-04
Weights	Open	Open
Context window	128K	131K
Max output	33K	16K
Input $ / M tokens	$0.30	$0.70
Output $ / M tokens	$0.90	$2.80
Results tracked	12	26

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.9 (#128), Qwen3 32B: 37.7 (#190)

Coding benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Coding	1390	1358
Aider Polyglot	—	40%
SciCode	—	35.4%

Agentic & Tool Use Not comparable

GLM-4.6V: —, Qwen3 32B: 32.6 (#62)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
Berkeley Function Calling Leaderboard	—	48.7%

Reasoning GLM-4.6V leads

GLM-4.6V: 27.6 (#115), Qwen3 32B: 20.2 (#241)

Reasoning benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Hard Prompts	1368	1334
Kagi LLM Benchmark	—	54.9%
CritPt	—	0.3%
Chess Puzzles	—	5%
DTBench	—	67.5%
LMCA	—	17.3%
Epoch Capabilities Index	—	138.51

Math Not comparable

GLM-4.6V: —, Qwen3 32B: 39.7 (#99)

Math benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
OTIS Mock AIME 2024-2025	—	66.9%
LMArena Math	—	1399

Knowledge Qwen3 32B leads

GLM-4.6V: 38.0 (#149), Qwen3 32B: 40.0 (#125)

Knowledge benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Expert	1371	1362
GPQA Diamond	—	65.7%
Vectara Hallucination Rate	—	5.9%

Multimodal Not comparable

GLM-4.6V: 34.8 (#90), Qwen3 32B: —

Multimodal benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Vision	1164	—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.6 (#141), Qwen3 32B: 45.6 (#167)

Multilingual benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Non-English	1359	1317
LMArena Chinese	1425	1357
LMArena Russian	1340	1311
LMArena German	—	1341

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.4 (#151), Qwen3 32B: 68.9 (#179)

Instruction Following benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Instruction Following	1352	1305

Long Context Qwen3 32B leads

GLM-4.6V: 41.3 (#143), Qwen3 32B: 43.8 (#87)

Long Context benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Longer Query	1358	1327
Fiction.LiveBench	—	74.2%

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.6 (#137), Qwen3 32B: 52.9 (#163)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	Qwen3 32B
LMArena Text	1377	1340
LMArena Creative Writing	1347	1297
LMArena Multi-Turn	1360	1331

Frequently asked questions

Is GLM-4.6V better than Qwen3 32B?

GLM-4.6V is the stronger model overall, scoring 41.3 to 39.2 on the Noometry Index.

Which is cheaper, GLM-4.6V or Qwen3 32B?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

Is GLM-4.6V or Qwen3 32B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 37.7 in the Noometry coding category.

Which has the bigger context window?

Qwen3 32B does, with 131K tokens against 128K.

How many benchmarks do GLM-4.6V and Qwen3 32B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3 32B has 26.