Model comparison

GLM-4.5 vs Llama 3.2 1B

GLM-4.5 is the stronger model overall, scoring 42.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 14× 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 1B

Summary

They share 14 benchmarks with published results for both. GLM-4.5 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-4.5 leads 57.5 to 21.3.
Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
GLM-4.5 accepts more context: 131K tokens versus 60K.

Side by side

GLM-4.5 and Llama 3.2 1B specifications
	GLM-4.5	Llama 3.2 1B
Provider	Z.ai (Zhipu)	Meta
Noometry Index	42.0	20.1
Released	2025-07-27	2024-09-24
Weights	Open	Open
Context window	131K	60K
Max output	98K	54K
Input $ / M tokens	$0.60	$0.027
Output $ / M tokens	$2.20	$0.20
Results tracked	27	22

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Llama 3.2 1B: 21.1 (#338)

Coding benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Coding	1434	1070
SWE-bench Verified (bash only)	54.2%	—
WeirdML	40.6%	—
BigCodeBench Instruct	—	8.2%
BigCodeBench Complete	—	11.3%
ALE-Bench	344.82	—
AlgoTune	1.52	—

Agentic & Tool Use Not comparable

GLM-4.5: —, Llama 3.2 1B: 14.6 (#150)

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

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Llama 3.2 1B: 16.2 (#308)

Reasoning benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Hard Prompts	1429	1044
Kagi LLM Benchmark	57.9%	—
Chess Puzzles	—	0%
Epoch Capabilities Index	—	101.99

Math GLM-4.5 leads

GLM-4.5: 39.0 (#116), Llama 3.2 1B: 10.4 (#313)

Math benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Math	1427	1086
OTIS Mock AIME 2024-2025	—	0.6%

Knowledge GLM-4.5 leads

GLM-4.5: 35.9 (#179), Llama 3.2 1B: 7.2 (#312)

Knowledge benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Expert	1433	1007
GPQA Diamond	—	23.9%
Humanity's Last Exam	8.3%	—
Confabulations	11.3%	—

Multilingual GLM-4.5 leads

GLM-4.5: 52.8 (#77), Llama 3.2 1B: 23.8 (#292)

Multilingual benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Non-English	1417	973
LMArena Chinese	1465	959
LMArena German	1407	1014
LMArena Russian	1414	941
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 1B: 52.4 (#290)

Instruction Following benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Instruction Following	1404	1031

Long Context GLM-4.5 leads

GLM-4.5: 38.2 (#201), Llama 3.2 1B: 31.9 (#274)

Long Context benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Longer Query	1412	1050
Fiction.LiveBench	58.3%	—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.5 (#127), Llama 3.2 1B: 21.3 (#310)

Writing & Preference benchmarks
Benchmark	GLM-4.5	Llama 3.2 1B
LMArena Text	1430	1055
LMArena Creative Writing	1395	1033
EQ-Bench Creative Writing	1343	200
LMArena Multi-Turn	1415	1030
Short-Story Creative Writing	73.4%	—

Frequently asked questions

Is GLM-4.5 better than Llama 3.2 1B?

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

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

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 21.1 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5 does, with 131K tokens against 60K.

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

14 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Llama 3.2 1B has 22.