Model comparison

GLM-5.2 vs Qwen2.5 32B Instruct

GLM-5.2 is the stronger model overall, scoring 51.1 to 30.1 on the Noometry Index. Qwen2.5 32B Instruct costs 1.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

Last verified October 11, 2026. 4 shared benchmarks.

GLM-5.2 Z.ai (Zhipu)

51.1

Rank #44 Confirmed

Qwen2.5 32B Instruct

Alibaba (Qwen)

30.1

Rank #297 Confirmed

Summary

They share 4 benchmarks with published results for both. GLM-5.2 scores higher in 4 categories and Qwen2.5 32B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
The widest gap is in math, where GLM-5.2 leads 55.7 to 16.2.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 7.4% for Qwen2.5 32B Instruct.
Qwen2.5 32B Instruct is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
GLM-5.2 accepts more context: 1M tokens versus 131K.

Side by side

GLM-5.2 and Qwen2.5 32B Instruct specifications
	GLM-5.2	Qwen2.5 32B Instruct
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	51.1	30.1
Released	2026-06-13	2024-09
Weights	Open	Open
Context window	1M	131K
Max output	131K	8K
Input $ / M tokens	$1.40	$0.70
Output $ / M tokens	$4.40	$2.80
Results tracked	51	7

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Category by category

Coding GLM-5.2 leads

GLM-5.2: 51.3 (#41), Qwen2.5 32B Instruct: 38.7 (#169)

Coding benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
SWE-bench Verified	78.7%	—
DeepSWE	43.8%	—
FrontierCode	24.5%	—
LMArena WebDev	1603	—
SciCode	50.5%	—
WeirdML	70.1%	—
BigCodeBench Instruct	—	45%
LMArena Coding	1485	—
BigCodeBench Complete	—	52.3%
ALE-Bench	1,047	—

Agentic & Tool Use Not comparable

GLM-5.2: 32.4 (#63), Qwen2.5 32B Instruct: —

Agentic & Tool Use benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
APEX-Agents	45.2%	—
τ²-bench Banking	37.1%	—
PostTrainBench	31.7%	—
GBAEval	0%	—
Vending-Bench 2	8,314	—

Reasoning GLM-5.2 leads

GLM-5.2: 42.3 (#52), Qwen2.5 32B Instruct: 19.2 (#266)

Reasoning benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
Chess Puzzles	21%	0%
Epoch Capabilities Index	151.78	128.52
ARC-AGI-2	22.8%	—
SimpleBench	58.8%	—
Kagi LLM Benchmark	62.6%	—
NYT Connections (extended)	74.3%	—
ARC-AGI-1	77%	—
CritPt	20.9%	—
EBR-Bench	9.5%	—
LMArena Hard Prompts	1480	—
Mystery Game Puzzles	19%	—
DTBench	93.6%	—
LMCA	45.8%	—
Surface Evolver Bench	55.6%	—

Math GLM-5.2 leads

GLM-5.2: 55.7 (#43), Qwen2.5 32B Instruct: 16.2 (#296)

Math benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
OTIS Mock AIME 2024-2025	86.4%	7.4%
FrontierMath (Tiers 1-3)	59.2%	—
FrontierMath Tier 4	29.3%	—
MathArena Final-Answer Competitions	67.6%	—
ProofBench	35%	—
LMArena Math	1482	—
MATH Level 5	—	56.1%

Knowledge GLM-5.2 leads

GLM-5.2: 57.1 (#40), Qwen2.5 32B Instruct: 24.9 (#266)

Knowledge benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
GPQA Diamond	91.9%	46.1%
SimpleQA Verified	34.2%	—
LMArena Expert	1486	—

Multilingual Not comparable

GLM-5.2: 55.8 (#26), Qwen2.5 32B Instruct: —

Multilingual benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
LMArena Non-English	1459	—
LMArena Chinese	1519	—
LMArena French	1479	—
LMArena German	1468	—
LMArena Japanese	1451	—
LMArena Korean	1445	—
LMArena Russian	1466	—
LMArena Spanish	1477	—

Instruction Following Not comparable

GLM-5.2: 76.9 (#34), Qwen2.5 32B Instruct: —

Instruction Following benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
LMArena Instruction Following	1465	—

Long Context Not comparable

GLM-5.2: 45.3 (#43), Qwen2.5 32B Instruct: —

Long Context benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
LMArena Longer Query	1479	—

Writing & Preference Not comparable

GLM-5.2: 70.4 (#21), Qwen2.5 32B Instruct: —

Writing & Preference benchmarks
Benchmark	GLM-5.2	Qwen2.5 32B Instruct
LMArena Text	1470	—
LMArena Creative Writing	1462	—
EQ-Bench Creative Writing	1757	—
EQ-Bench 4	1222	—
LMArena Multi-Turn	1469	—

Frequently asked questions

Is GLM-5.2 better than Qwen2.5 32B Instruct?

Which is cheaper, GLM-5.2 or Qwen2.5 32B Instruct?

Qwen2.5 32B Instruct is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

Is GLM-5.2 or Qwen2.5 32B Instruct better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 38.7 in the Noometry coding category.

Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 131K.

How many benchmarks do GLM-5.2 and Qwen2.5 32B Instruct share?

4 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Qwen2.5 32B Instruct has 7.