Model comparison

GLM-4.6 vs Qwen2.5 32B Instruct

GLM-4.6 is the stronger model overall, scoring 41.4 to 30.1 on the Noometry Index.

Last verified October 10, 2026. 0 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen2.5 32B Instruct

Alibaba (Qwen)

30.1

Rank #297 Confirmed

Summary

The widest gap is in math, where GLM-4.6 leads 39.1 to 16.2.
GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
GLM-4.6 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.6 and Qwen2.5 32B Instruct specifications
	GLM-4.6	Qwen2.5 32B Instruct
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	41.4	30.1
Released	2025-09-30	2024-09
Weights	Open	Open
Context window	205K	131K
Max output	131K	8K
Input $ / M tokens	$0.60	$0.70
Output $ / M tokens	$2.20	$2.80
Results tracked	29	7

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Qwen2.5 32B Instruct: 38.7 (#169)

Coding benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
SWE-bench Verified (bash only)	55.4%	—
LMArena WebDev	1340	—
SciCode	38.4%	—
BigCodeBench Instruct	—	45%
LMArena Coding	1449	—
BigCodeBench Complete	—	52.3%
ALE-Bench	340.82	—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Qwen2.5 32B Instruct: —

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
Terminal-Bench	24.5%	—
Berkeley Function Calling Leaderboard	72.4%	—

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Qwen2.5 32B Instruct: 19.2 (#266)

Reasoning benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
Kagi LLM Benchmark	47.4%	—
CritPt	1.1%	—
Chess Puzzles	—	0%
LMArena Hard Prompts	1440	—
Epoch Capabilities Index	—	128.52

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Qwen2.5 32B Instruct: 16.2 (#296)

Math benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
OTIS Mock AIME 2024-2025	—	7.4%
LMArena Math	1432	—
MATH Level 5	—	56.1%
FrontierMath (Feb 2025 set)	3.8%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Qwen2.5 32B Instruct: 24.9 (#266)

Knowledge benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
GPQA Diamond	—	46.1%
Vectara Hallucination Rate	9.5%	—
LMArena Expert	1431	—

Multilingual Not comparable

GLM-4.6: 53.5 (#66), Qwen2.5 32B Instruct: —

Multilingual benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
LMArena Non-English	1426	—
LMArena Chinese	1499	—
LMArena French	1459	—
LMArena German	1447	—
LMArena Japanese	1393	—
LMArena Korean	1400	—
LMArena Russian	1419	—
LMArena Spanish	1436	—

Instruction Following Not comparable

GLM-4.6: 74.3 (#98), Qwen2.5 32B Instruct: —

Instruction Following benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
LMArena Instruction Following	1410	—

Long Context Not comparable

GLM-4.6: 43.4 (#94), Qwen2.5 32B Instruct: —

Long Context benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
LMArena Longer Query	1422	—

Writing & Preference Not comparable

GLM-4.6: 61.1 (#90), Qwen2.5 32B Instruct: —

Writing & Preference benchmarks
Benchmark	GLM-4.6	Qwen2.5 32B Instruct
LMArena Text	1440	—
LMArena Creative Writing	1411	—
EQ-Bench Creative Writing	1411	—
LMArena Multi-Turn	1427	—

Frequently asked questions

Is GLM-4.6 better than Qwen2.5 32B Instruct?

GLM-4.6 is the stronger model overall, scoring 41.4 to 30.1 on the Noometry Index.

Which is cheaper, GLM-4.6 or Qwen2.5 32B Instruct?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.

Is GLM-4.6 or Qwen2.5 32B Instruct better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 38.7 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

How many benchmarks do GLM-4.6 and Qwen2.5 32B Instruct share?

0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen2.5 32B Instruct has 7.