Model comparison

GLM-4.5 vs Qwen2.5 7B Instruct

GLM-4.5 is the stronger model overall, scoring 42.0 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 3.3× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.

Last verified October 10, 2026. 0 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Qwen2.5 7B Instruct

Alibaba (Qwen)

29.0

Rank #320 Confirmed

Summary

The widest gap is in math, where GLM-4.5 leads 39.0 to 12.6.
Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.

Side by side

GLM-4.5 and Qwen2.5 7B Instruct specifications
	GLM-4.5	Qwen2.5 7B Instruct
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	42.0	29.0
Released	2025-07-27	2024-09
Weights	Open	Open
Context window	131K	131K
Max output	98K	8K
Input $ / M tokens	$0.60	$0.17
Output $ / M tokens	$2.20	$0.70
Results tracked	27	15

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Qwen2.5 7B Instruct: 36.5 (#208)

Coding benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
SWE-bench Verified (bash only)	54.2%	—
WeirdML	40.6%	—
BigCodeBench Instruct	—	37.6%
LMArena Coding	1434	—
BigCodeBench Complete	—	46.1%
ALE-Bench	344.82	—
AlgoTune	1.52	—

Agentic & Tool Use Not comparable

GLM-4.5: —, Qwen2.5 7B Instruct: 23.8 (#124)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
BALROG	—	7.8%

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Qwen2.5 7B Instruct: 14.8 (#322)

Reasoning benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
Kagi LLM Benchmark	57.9%	—
Chess Puzzles	—	0%
LMArena Hard Prompts	1429	—
DTBench	—	47.7%
LMCA	—	6.4%
Epoch Capabilities Index	—	118.51

Math GLM-4.5 leads

GLM-4.5: 39.0 (#116), Qwen2.5 7B Instruct: 12.6 (#306)

Math benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
OTIS Mock AIME 2024-2025	—	2.5%
Omni-MATH	—	29.4%
LMArena Math	1427	—

Knowledge GLM-4.5 leads

GLM-4.5: 35.9 (#179), Qwen2.5 7B Instruct: 17.0 (#286)

Knowledge benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
GPQA Diamond	—	35.5%
Humanity's Last Exam	8.3%	—
MMLU-Pro	—	53.9%
Confabulations	11.3%	—
GPQA (HELM)	—	34.1%
LMArena Expert	1433	—
MMLU	—	72.9%

Multilingual Not comparable

GLM-4.5: 52.8 (#77), Qwen2.5 7B Instruct: —

Multilingual benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
LMArena Non-English	1417	—
LMArena Chinese	1465	—
LMArena French	1418	—
LMArena German	1407	—
LMArena Japanese	1415	—
LMArena Korean	1380	—
LMArena Russian	1414	—
LMArena Spanish	1454	—

Instruction Following GLM-4.5 leads

GLM-4.5: 74.1 (#104), Qwen2.5 7B Instruct: 63.2 (#231)

Instruction Following benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
IFEval	—	74.1%
LMArena Instruction Following	1404	—

Long Context Not comparable

GLM-4.5: 38.2 (#201), Qwen2.5 7B Instruct: —

Long Context benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
Fiction.LiveBench	58.3%	—
LMArena Longer Query	1412	—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.5 (#127), Qwen2.5 7B Instruct: 48.8 (#195)

Writing & Preference benchmarks
Benchmark	GLM-4.5	Qwen2.5 7B Instruct
LMArena Text	1430	—
LMArena Creative Writing	1395	—
Short-Story Creative Writing	73.4%	—
EQ-Bench Creative Writing	1343	—
WildBench	—	73.1%
LMArena Multi-Turn	1415	—

Frequently asked questions

Is GLM-4.5 better than Qwen2.5 7B Instruct?

Which is cheaper, GLM-4.5 or Qwen2.5 7B Instruct?

Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

Is GLM-4.5 or Qwen2.5 7B Instruct better for coding?

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 36.5 in the Noometry coding category.

Which has the bigger context window?

Both accept 131K tokens.

How many benchmarks do GLM-4.5 and Qwen2.5 7B Instruct share?

0 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen2.5 7B Instruct has 15.