Model comparison

GLM-4.5V vs Qwen2.5 7B Instruct

GLM-4.5V is the stronger model overall, scoring 39.8 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.9× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Last verified October 11, 2026. 0 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Qwen2.5 7B Instruct

Alibaba (Qwen)

29.0

Rank #320 Confirmed

Summary

The widest gap is in math, where GLM-4.5V leads 37.4 to 12.6.
Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
Qwen2.5 7B Instruct accepts more context: 131K tokens versus 64K.

Side by side

GLM-4.5V and Qwen2.5 7B Instruct specifications
	GLM-4.5V	Qwen2.5 7B Instruct
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	39.8	29.0
Released	2025-08-11	2024-09
Weights	Open	Open
Context window	64K	131K
Max output	16K	8K
Input $ / M tokens	$0.60	$0.17
Output $ / M tokens	$1.80	$0.70
Results tracked	15	15

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Qwen2.5 7B Instruct: 36.5 (#208)

Coding benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
BigCodeBench Instruct	—	37.6%
LMArena Coding	1347	—
BigCodeBench Complete	—	46.1%

Agentic & Tool Use Not comparable

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

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

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), Qwen2.5 7B Instruct: 14.8 (#322)

Reasoning benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
Kagi LLM Benchmark	59.8%	—
Chess Puzzles	—	0%
LMArena Hard Prompts	1334	—
DTBench	—	47.7%
LMCA	—	6.4%
Epoch Capabilities Index	—	118.51

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), Qwen2.5 7B Instruct: 12.6 (#306)

Math benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
OTIS Mock AIME 2024-2025	—	2.5%
Omni-MATH	—	29.4%
LMArena Math	1354	—

Knowledge GLM-4.5V leads

GLM-4.5V: 37.5 (#156), Qwen2.5 7B Instruct: 17.0 (#286)

Knowledge benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
GPQA Diamond	—	35.5%
MMLU-Pro	—	53.9%
GPQA (HELM)	—	34.1%
LMArena Expert	1353	—
MMLU	—	72.9%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Qwen2.5 7B Instruct: —

Multimodal benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
LMArena Vision	1154	—

Multilingual Not comparable

GLM-4.5V: 44.6 (#177), Qwen2.5 7B Instruct: —

Multilingual benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
LMArena Non-English	1303	—
LMArena Chinese	1337	—
LMArena Russian	1298	—
LMArena Spanish	1336	—

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.2 (#175), Qwen2.5 7B Instruct: 63.2 (#231)

Instruction Following benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
IFEval	—	74.1%
LMArena Instruction Following	1311	—

Long Context Not comparable

GLM-4.5V: 39.6 (#171), Qwen2.5 7B Instruct: —

Long Context benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
LMArena Longer Query	1304	—

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.5 (#170), Qwen2.5 7B Instruct: 48.8 (#195)

Writing & Preference benchmarks
Benchmark	GLM-4.5V	Qwen2.5 7B Instruct
LMArena Text	1333	—
LMArena Creative Writing	1295	—
WildBench	—	73.1%
LMArena Multi-Turn	1332	—

Frequently asked questions

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

Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.

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

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 36.5 in the Noometry coding category.

Which has the bigger context window?

Qwen2.5 7B Instruct does, with 131K tokens against 64K.

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

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