Model comparison

GLM-4.6V vs Llama 3.2 3B

GLM-4.6V is the stronger model overall, scoring 41.3 to 28.9 on the Noometry Index. Llama 3.2 3B costs 3.7× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

Last verified October 11, 2026. 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

Llama 3.2 3B

Summary

They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3.2 3B in 0 categories; 7 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.6V leads 56.6 to 24.7.
Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
Llama 3.2 3B accepts more context: 131K tokens versus 128K.

Side by side

GLM-4.6V and Llama 3.2 3B specifications
	GLM-4.6V	Llama 3.2 3B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	41.3	28.9
Released	2025-12-08	2024-09-24
Weights	Open	Open
Context window	128K	131K
Max output	33K	118K
Input $ / M tokens	$0.30	$0.05
Output $ / M tokens	$0.90	$0.33
Results tracked	12	18

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.9 (#128), Llama 3.2 3B: 27.6 (#319)

Coding benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Coding	1390	1098
BigCodeBench Instruct	—	23.4%
BigCodeBench Complete	—	28.3%

Agentic & Tool Use Not comparable

GLM-4.6V: —, Llama 3.2 3B: 20.1 (#143)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
Berkeley Function Calling Leaderboard	—	21.9%
BALROG	—	10.1%

Reasoning GLM-4.6V leads

GLM-4.6V: 27.6 (#115), Llama 3.2 3B: 21.0 (#228)

Reasoning benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Hard Prompts	1368	1095

Math Not comparable

GLM-4.6V: —, Llama 3.2 3B: 32.4 (#214)

Math benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Math	—	1126

Knowledge GLM-4.6V leads

GLM-4.6V: 38.0 (#149), Llama 3.2 3B: 29.7 (#235)

Knowledge benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Expert	1371	1090

Multimodal Not comparable

GLM-4.6V: 34.8 (#90), Llama 3.2 3B: —

Multimodal benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Vision	1164	—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.6 (#141), Llama 3.2 3B: 26.2 (#281)

Multilingual benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Non-English	1359	1019
LMArena Chinese	1425	1017
LMArena Russian	1340	949
LMArena German	—	1056

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.4 (#151), Llama 3.2 3B: 56.0 (#275)

Instruction Following benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Instruction Following	1352	1089

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#143), Llama 3.2 3B: 33.4 (#261)

Long Context benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Longer Query	1358	1100

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.6 (#137), Llama 3.2 3B: 24.7 (#307)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	Llama 3.2 3B
LMArena Text	1377	1110
LMArena Creative Writing	1347	1094
LMArena Multi-Turn	1360	1105
EQ-Bench Creative Writing	—	595

Frequently asked questions

Is GLM-4.6V better than Llama 3.2 3B?

Which is cheaper, GLM-4.6V or Llama 3.2 3B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

Is GLM-4.6V or Llama 3.2 3B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 27.6 in the Noometry coding category.

Which has the bigger context window?

Llama 3.2 3B does, with 131K tokens against 128K.

How many benchmarks do GLM-4.6V and Llama 3.2 3B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.2 3B has 18.