Model comparison

GLM-4.6 vs Qwen2.5 72B Instruct

GLM-4.6 is the stronger model overall, scoring 41.4 to 31.9 on the Noometry Index.

Last verified October 11, 2026. 17 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen2.5 72B Instruct

Alibaba (Qwen)

31.9

Rank #267 Confirmed

Summary

They share 17 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and Qwen2.5 72B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
The widest gap is in math, where GLM-4.6 leads 39.1 to 19.3.
GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
GLM-4.6 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.6 and Qwen2.5 72B Instruct specifications
	GLM-4.6	Qwen2.5 72B Instruct
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	41.4	31.9
Released	2025-09-30	2024-09
Weights	Open	Open
Context window	205K	131K
Max output	131K	8K
Input $ / M tokens	$0.60	$1.40
Output $ / M tokens	$2.20	$5.60
Results tracked	29	43

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Qwen2.5 72B Instruct: 33.2 (#260)

Coding benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Coding	1449	1292
SWE-bench Verified (bash only)	55.4%	—
LMArena WebDev	1340	—
SciCode	38.4%	—
WeirdML	—	16%
BigCodeBench Instruct	—	45.8%
BigCodeBench Complete	—	55.9%
ALE-Bench	340.82	—

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.3 (#66), Qwen2.5 72B Instruct: 22.1 (#133)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
Terminal-Bench	24.5%	—
Berkeley Function Calling Leaderboard	72.4%	—
TheAgentCompany	—	5.7%
BALROG	—	16.2%
METR Time Horizons	—	35.8%

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Qwen2.5 72B Instruct: 22.3 (#199)

Reasoning benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Hard Prompts	1440	1271
Kagi LLM Benchmark	47.4%	—
CritPt	1.1%	—
DTBench	—	62.9%
LMCA	—	13.4%
BIG-Bench Hard	—	79.8%
Epoch Capabilities Index	—	129
ForecastBench	—	57.5
HellaSwag	—	84.8%
PIQA	—	82.6%
WinoGrande	—	82.3%

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Qwen2.5 72B Instruct: 19.3 (#287)

Math benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Math	1432	1283
OTIS Mock AIME 2024-2025	—	8.1%
Omni-MATH	—	33%
MATH Level 5	—	63.2%
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 72B Instruct: 27.0 (#253)

Knowledge benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Expert	1431	1245
GPQA Diamond	—	49.1%
MMLU-Pro	—	63.1%
Confabulations	—	19.1%
Vectara Hallucination Rate	9.5%	—
GPQA (HELM)	—	42.6%
ARC (AI2) Challenge	—	94.5%
MMLU	—	85.3%
TriviaQA	—	71.9%

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Qwen2.5 72B Instruct: 41.0 (#213)

Multilingual benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Non-English	1426	1252
LMArena Chinese	1499	1272
LMArena French	1459	1280
LMArena German	1447	1234
LMArena Japanese	1393	1180
LMArena Korean	1400	1188
LMArena Russian	1419	1264
LMArena Spanish	1436	1256

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Qwen2.5 72B Instruct: 65.5 (#221)

Instruction Following benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Instruction Following	1410	1254
IFEval	—	80.6%

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), Qwen2.5 72B Instruct: 38.9 (#188)

Long Context benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Longer Query	1422	1282

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Qwen2.5 72B Instruct: 46.7 (#215)

Writing & Preference benchmarks
Benchmark	GLM-4.6	Qwen2.5 72B Instruct
LMArena Text	1440	1269
LMArena Creative Writing	1411	1221
LMArena Multi-Turn	1427	1272
EQ-Bench Creative Writing	1411	—
WildBench	—	80.2%

Frequently asked questions

Is GLM-4.6 better than Qwen2.5 72B Instruct?

GLM-4.6 is the stronger model overall, scoring 41.4 to 31.9 on the Noometry Index.

Which is cheaper, GLM-4.6 or Qwen2.5 72B Instruct?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.

Is GLM-4.6 or Qwen2.5 72B Instruct better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 33.2 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 72B Instruct share?

17 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen2.5 72B Instruct has 43.