Model comparison

GLM-4.5 vs Llama 3.2 3B

GLM-4.5 is the stronger model overall, scoring 42.0 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-4.5's lead doesn't matter for your workload.

Last verified October 10, 2026. 14 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Llama 3.2 3B

Summary

They share 14 benchmarks with published results for both. GLM-4.5 scores higher in 8 categories and Llama 3.2 3B in 0 categories; 8 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.5 leads 57.5 to 24.7.
Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.

Side by side

GLM-4.5 and Llama 3.2 3B specifications
	GLM-4.5	Llama 3.2 3B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	42.0	28.9
Released	2025-07-27	2024-09-24
Weights	Open	Open
Context window	131K	131K
Max output	98K	118K
Input $ / M tokens	$0.60	$0.05
Output $ / M tokens	$2.20	$0.33
Results tracked	27	18

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Llama 3.2 3B: 27.6 (#319)

Coding benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Coding	1434	1098
SWE-bench Verified (bash only)	54.2%	—
WeirdML	40.6%	—
BigCodeBench Instruct	—	23.4%
BigCodeBench Complete	—	28.3%
ALE-Bench	344.82	—
AlgoTune	1.52	—

Agentic & Tool Use Not comparable

GLM-4.5: —, Llama 3.2 3B: 20.1 (#143)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
Berkeley Function Calling Leaderboard	—	21.9%
BALROG	—	10.1%

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Llama 3.2 3B: 21.0 (#228)

Reasoning benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Hard Prompts	1429	1095
Kagi LLM Benchmark	57.9%	—

Math GLM-4.5 leads

GLM-4.5: 39.0 (#116), Llama 3.2 3B: 32.4 (#214)

Math benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Math	1427	1126

Knowledge GLM-4.5 leads

GLM-4.5: 35.9 (#179), Llama 3.2 3B: 29.7 (#235)

Knowledge benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Expert	1433	1090
Humanity's Last Exam	8.3%	—
Confabulations	11.3%	—

Multilingual GLM-4.5 leads

GLM-4.5: 52.8 (#77), Llama 3.2 3B: 26.2 (#281)

Multilingual benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Non-English	1417	1019
LMArena Chinese	1465	1017
LMArena German	1407	1056
LMArena Russian	1414	949
LMArena French	1418	—
LMArena Japanese	1415	—
LMArena Korean	1380	—
LMArena Spanish	1454	—

Instruction Following GLM-4.5 leads

GLM-4.5: 74.1 (#104), Llama 3.2 3B: 56.0 (#275)

Instruction Following benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Instruction Following	1404	1089

Long Context GLM-4.5 leads

GLM-4.5: 38.2 (#201), Llama 3.2 3B: 33.4 (#261)

Long Context benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Longer Query	1412	1100
Fiction.LiveBench	58.3%	—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.5 (#127), Llama 3.2 3B: 24.7 (#307)

Writing & Preference benchmarks
Benchmark	GLM-4.5	Llama 3.2 3B
LMArena Text	1430	1110
LMArena Creative Writing	1395	1094
EQ-Bench Creative Writing	1343	595
LMArena Multi-Turn	1415	1105
Short-Story Creative Writing	73.4%	—

Frequently asked questions

Is GLM-4.5 better than Llama 3.2 3B?

Which is cheaper, GLM-4.5 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-4.5 lists at $0.60 and $2.20.

Is GLM-4.5 or Llama 3.2 3B better for coding?

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 27.6 in the Noometry coding category.

Which has the bigger context window?

Both accept 131K tokens.

How many benchmarks do GLM-4.5 and Llama 3.2 3B share?

14 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Llama 3.2 3B has 18.