Model comparison

GLM-4.6 vs Llama 3.2 3B

GLM-4.6 is the stronger model overall, scoring 41.4 to 28.9 on the Noometry Index. Llama 3.2 3B costs 8.3× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified October 10, 2026. 15 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Llama 3.2 3B

Summary

They share 15 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and Llama 3.2 3B in 0 categories; 9 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 24.7.
The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 21.9% for Llama 3.2 3B.
Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
GLM-4.6 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.6 and Llama 3.2 3B specifications
	GLM-4.6	Llama 3.2 3B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	41.4	28.9
Released	2025-09-30	2024-09-24
Weights	Open	Open
Context window	205K	131K
Max output	131K	118K
Input $ / M tokens	$0.60	$0.05
Output $ / M tokens	$2.20	$0.33
Results tracked	29	18

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Llama 3.2 3B: 27.6 (#319)

Coding benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Coding	1449	1098
SWE-bench Verified (bash only)	55.4%	—
LMArena WebDev	1340	—
SciCode	38.4%	—
BigCodeBench Instruct	—	23.4%
BigCodeBench Complete	—	28.3%
ALE-Bench	340.82	—

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.3 (#66), Llama 3.2 3B: 20.1 (#143)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
Berkeley Function Calling Leaderboard	72.4%	21.9%
Terminal-Bench	24.5%	—
BALROG	—	10.1%

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Llama 3.2 3B: 21.0 (#228)

Reasoning benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Hard Prompts	1440	1095
Kagi LLM Benchmark	47.4%	—
CritPt	1.1%	—

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Llama 3.2 3B: 32.4 (#214)

Math benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Math	1432	1126
FrontierMath (Feb 2025 set)	3.8%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Llama 3.2 3B: 29.7 (#235)

Knowledge benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Expert	1431	1090
Vectara Hallucination Rate	9.5%	—

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Llama 3.2 3B: 26.2 (#281)

Multilingual benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Non-English	1426	1019
LMArena Chinese	1499	1017
LMArena German	1447	1056
LMArena Russian	1419	949
LMArena French	1459	—
LMArena Japanese	1393	—
LMArena Korean	1400	—
LMArena Spanish	1436	—

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Llama 3.2 3B: 56.0 (#275)

Instruction Following benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Instruction Following	1410	1089

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), Llama 3.2 3B: 33.4 (#261)

Long Context benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Longer Query	1422	1100

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Llama 3.2 3B: 24.7 (#307)

Writing & Preference benchmarks
Benchmark	GLM-4.6	Llama 3.2 3B
LMArena Text	1440	1110
LMArena Creative Writing	1411	1094
EQ-Bench Creative Writing	1411	595
LMArena Multi-Turn	1427	1105

Frequently asked questions

Is GLM-4.6 better than Llama 3.2 3B?

Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.

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

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 27.6 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

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

15 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Llama 3.2 3B has 18.