Model comparison

Llama 3.2 1B vs o3-pro

o3-pro is the stronger model overall, scoring 42.9 to 20.1 on the Noometry Index. Llama 3.2 1B costs 496× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

Last verified October 11, 2026. 1 shared benchmarks.

Llama 3.2 1B

Summary

They share 1 benchmark with published results for both. Llama 3.2 1B scores higher in 0 categories and o3-pro in 5 categories; 5 gaps are clear of the uncertainty.
The widest gap is in long context, where o3-pro leads 72.2 to 31.9.
Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $20 / $80 for o3-pro.
o3-pro accepts more context: 200K tokens versus 60K.
Llama 3.2 1B has downloadable open weights; the other is API-only.

Side by side

Llama 3.2 1B and o3-pro specifications
	Llama 3.2 1B	o3-pro
Provider	Meta	OpenAI
Noometry Index	20.1	42.9
Released	2024-09-24	2025-06-10
Weights	Open	Proprietary
Context window	60K	200K
Max output	54K	100K
Input $ / M tokens	$0.027	$20
Output $ / M tokens	$0.20	$80
Results tracked	22	12

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Category by category

Coding o3-pro leads

Llama 3.2 1B: 21.1 (#338), o3-pro: 55.5 (#24)

Coding benchmarks
Benchmark	Llama 3.2 1B	o3-pro
Aider Polyglot	—	84.9%
WeirdML	—	58.2%
BigCodeBench Instruct	8.2%	—
LMArena Coding	1070	—
BigCodeBench Complete	11.3%	—

Agentic & Tool Use Not comparable

Llama 3.2 1B: 14.6 (#150), o3-pro: —

Agentic & Tool Use benchmarks
Benchmark	Llama 3.2 1B	o3-pro
Berkeley Function Calling Leaderboard	10.8%	—
BALROG	6.6%	—

Reasoning o3-pro leads

Llama 3.2 1B: 16.2 (#308), o3-pro: 23.8 (#171)

Reasoning benchmarks
Benchmark	Llama 3.2 1B	o3-pro
Epoch Capabilities Index	101.99	147.42
ARC-AGI-2	—	4.9%
Kagi LLM Benchmark	—	72.1%
ARC-AGI-1	—	59.3%
Chess Puzzles	0%	—
LMArena Hard Prompts	1044	—
DTBench	—	86.9%
LMCA	—	38.5%

Math Not comparable

Llama 3.2 1B: 10.4 (#313), o3-pro: —

Math benchmarks
Benchmark	Llama 3.2 1B	o3-pro
OTIS Mock AIME 2024-2025	0.6%	—
LMArena Math	1086	—

Knowledge o3-pro leads

Llama 3.2 1B: 7.2 (#312), o3-pro: 29.5 (#238)

Knowledge benchmarks
Benchmark	Llama 3.2 1B	o3-pro
GPQA Diamond	23.9%	—
Confabulations	—	14.2%
Vectara Hallucination Rate	—	23.3%
LMArena Expert	1007	—

Multilingual Not comparable

Llama 3.2 1B: 23.8 (#292), o3-pro: —

Multilingual benchmarks
Benchmark	Llama 3.2 1B	o3-pro
LMArena Non-English	973	—
LMArena Chinese	959	—
LMArena German	1014	—
LMArena Russian	941	—

Instruction Following Not comparable

Llama 3.2 1B: 52.4 (#290), o3-pro: —

Instruction Following benchmarks
Benchmark	Llama 3.2 1B	o3-pro
LMArena Instruction Following	1031	—

Long Context o3-pro leads

Llama 3.2 1B: 31.9 (#274), o3-pro: 72.2 (#1)

Long Context benchmarks
Benchmark	Llama 3.2 1B	o3-pro
Fiction.LiveBench	—	97.2%
LMArena Longer Query	1050	—

Writing & Preference o3-pro leads

Llama 3.2 1B: 21.3 (#310), o3-pro: 57.1 (#133)

Writing & Preference benchmarks
Benchmark	Llama 3.2 1B	o3-pro
LMArena Text	1055	—
LMArena Creative Writing	1033	—
Short-Story Creative Writing	—	84.4%
EQ-Bench Creative Writing	200	—
LMArena Multi-Turn	1030	—

Frequently asked questions

Is Llama 3.2 1B better than o3-pro?

Which is cheaper, Llama 3.2 1B or o3-pro?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; o3-pro lists at $20 and $80.

Is Llama 3.2 1B or o3-pro better for coding?

o3-pro scores higher on coding benchmarks: 55.5 versus 21.1 in the Noometry coding category.

Which has the bigger context window?

o3-pro does, with 200K tokens against 60K.

How many benchmarks do Llama 3.2 1B and o3-pro share?

1 benchmark has published results for both models. Llama 3.2 1B has 22 scored results on Noometry and o3-pro has 12.