Model comparison

GLM-4.5V vs Llama 3.2 1B

GLM-4.5V is the stronger model overall, scoring 39.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 13× 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. 12 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Llama 3.2 1B

Summary

They share 12 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 3.2 1B in 0 categories; 8 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.5V leads 52.5 to 21.3.
Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
GLM-4.5V accepts more context: 64K tokens versus 60K.

Side by side

GLM-4.5V and Llama 3.2 1B specifications
	GLM-4.5V	Llama 3.2 1B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	39.8	20.1
Released	2025-08-11	2024-09-24
Weights	Open	Open
Context window	64K	60K
Max output	16K	54K
Input $ / M tokens	$0.60	$0.027
Output $ / M tokens	$1.80	$0.20
Results tracked	15	22

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Llama 3.2 1B: 21.1 (#338)

Coding benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Coding	1347	1070
BigCodeBench Instruct	—	8.2%
BigCodeBench Complete	—	11.3%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Llama 3.2 1B: 14.6 (#150)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
Berkeley Function Calling Leaderboard	—	10.8%
BALROG	—	6.6%

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), Llama 3.2 1B: 16.2 (#308)

Reasoning benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Hard Prompts	1334	1044
Kagi LLM Benchmark	59.8%	—
Chess Puzzles	—	0%
Epoch Capabilities Index	—	101.99

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), Llama 3.2 1B: 10.4 (#313)

Math benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Math	1354	1086
OTIS Mock AIME 2024-2025	—	0.6%

Knowledge GLM-4.5V leads

GLM-4.5V: 37.5 (#156), Llama 3.2 1B: 7.2 (#312)

Knowledge benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Expert	1353	1007
GPQA Diamond	—	23.9%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Llama 3.2 1B: —

Multimodal benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Vision	1154	—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.6 (#177), Llama 3.2 1B: 23.8 (#292)

Multilingual benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Non-English	1303	973
LMArena Chinese	1337	959
LMArena Russian	1298	941
LMArena German	—	1014
LMArena Spanish	1336	—

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.2 (#175), Llama 3.2 1B: 52.4 (#290)

Instruction Following benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Instruction Following	1311	1031

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#171), Llama 3.2 1B: 31.9 (#274)

Long Context benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Longer Query	1304	1050

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.5 (#170), Llama 3.2 1B: 21.3 (#310)

Writing & Preference benchmarks
Benchmark	GLM-4.5V	Llama 3.2 1B
LMArena Text	1333	1055
LMArena Creative Writing	1295	1033
LMArena Multi-Turn	1332	1030
EQ-Bench Creative Writing	—	200

Frequently asked questions

Is GLM-4.5V better than Llama 3.2 1B?

Which is cheaper, GLM-4.5V or Llama 3.2 1B?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Llama 3.2 1B better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 21.1 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5V does, with 64K tokens against 60K.

How many benchmarks do GLM-4.5V and Llama 3.2 1B share?

12 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 3.2 1B has 22.