Model comparison

GLM-4.6V vs Llama 3.2 1B

GLM-4.6V is the stronger model overall, scoring 40.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 6.4× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

Last verified October 10, 2026. 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Llama 3.2 1B

Summary

They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3.2 1B in 0 categories; 7 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.6V leads 56.8 to 21.4.
Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
GLM-4.6V accepts more context: 128K tokens versus 60K.

Side by side

GLM-4.6V and Llama 3.2 1B specifications
	GLM-4.6V	Llama 3.2 1B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	40.9	19.9
Released	2025-12-08	2024-09-24
Weights	Open	Open
Context window	128K	60K
Max output	33K	54K
Input $ / M tokens	$0.30	$0.027
Output $ / M tokens	$0.90	$0.20
Results tracked	12	22

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Llama 3.2 1B: 20.6 (#333)

Coding benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Coding	1390	1071
BigCodeBench Instruct	—	8.2%
BigCodeBench Complete	—	11.3%

Agentic & Tool Use Not comparable

GLM-4.6V: —, Llama 3.2 1B: 14.5 (#142)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
Berkeley Function Calling Leaderboard	—	10.8%
BALROG	—	6.6%

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), Llama 3.2 1B: 15.6 (#304)

Reasoning benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Hard Prompts	1368	1044
Chess Puzzles	—	0%
Epoch Capabilities Index	—	101.95

Math Not comparable

GLM-4.6V: —, Llama 3.2 1B: 9.7 (#308)

Math benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
OTIS Mock AIME 2024-2025	—	0.6%
LMArena Math	—	1086

Knowledge GLM-4.6V leads

GLM-4.6V: 37.4 (#148), Llama 3.2 1B: 7.2 (#307)

Knowledge benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Expert	1372	1007
GPQA Diamond	—	23.9%

Multimodal Not comparable

GLM-4.6V: 34.2 (#83), Llama 3.2 1B: —

Multimodal benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Vision	1164	—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.8 (#135), Llama 3.2 1B: 23.9 (#287)

Multilingual benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Non-English	1359	973
LMArena Chinese	1426	958
LMArena Russian	1341	941
LMArena German	—	1014

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.6 (#145), Llama 3.2 1B: 52.6 (#285)

Instruction Following benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Instruction Following	1351	1031

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#138), Llama 3.2 1B: 31.9 (#269)

Long Context benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Longer Query	1358	1050

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.8 (#132), Llama 3.2 1B: 21.4 (#305)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	Llama 3.2 1B
LMArena Text	1377	1055
LMArena Creative Writing	1347	1033
LMArena Multi-Turn	1359	1030
EQ-Bench Creative Writing	—	200

Frequently asked questions

Is GLM-4.6V better than Llama 3.2 1B?

Which is cheaper, GLM-4.6V 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-4.6V lists at $0.30 and $0.90.

Is GLM-4.6V or Llama 3.2 1B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 20.6 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6V does, with 128K tokens against 60K.

How many benchmarks do GLM-4.6V and Llama 3.2 1B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.2 1B has 22.