Model comparison
Llama 3.2 1B vs o1-pro
o1-pro is the stronger model overall, scoring 31.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3723× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
MetaSummary
- The widest gap is in knowledge, where o1-pro leads 29.7 to 7.2.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-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 | o1-pro | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 20.1 | 31.5 |
| Released | 2024-09-24 | 2025-03-19 |
| Weights | Open | Proprietary |
| Context window | 60K | 200K |
| Max output | 54K | 100K |
| Input $ / M tokens | $0.027 | $150 |
| Output $ / M tokens | $0.20 | $600 |
| Results tracked | 22 | 3 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
Llama 3.2 1B: 21.1 (#338), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| BigCodeBench Instruct | 8.2% | — |
| LMArena Coding | 1070 | — |
| BigCodeBench Complete | 11.3% | — |
Agentic & Tool Use Not comparable
Llama 3.2 1B: 14.6 (#150), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
Reasoning o1-pro leads
Llama 3.2 1B: 16.2 (#308), o1-pro: 20.4 (#239)
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| ARC-AGI-1 | — | 23.3% |
| Chess Puzzles | 0% | — |
| EnigmaEval | — | 6.1% |
| LMArena Hard Prompts | 1044 | — |
| Epoch Capabilities Index | 101.99 | — |
Math Not comparable
Llama 3.2 1B: 10.4 (#313), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | — |
| LMArena Math | 1086 | — |
Knowledge o1-pro leads
Llama 3.2 1B: 7.2 (#312), o1-pro: 29.7 (#234)
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| GPQA Diamond | 23.9% | — |
| Humanity's Last Exam | — | 8.1% |
| LMArena Expert | 1007 | — |
Multilingual Not comparable
Llama 3.2 1B: 23.8 (#292), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-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), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| LMArena Instruction Following | 1031 | — |
Long Context Not comparable
Llama 3.2 1B: 31.9 (#274), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| LMArena Longer Query | 1050 | — |
Writing & Preference Not comparable
Llama 3.2 1B: 21.3 (#310), o1-pro: —
| Benchmark | Llama 3.2 1B | o1-pro |
|---|---|---|
| LMArena Text | 1055 | — |
| LMArena Creative Writing | 1033 | — |
| EQ-Bench Creative Writing | 200 | — |
| LMArena Multi-Turn | 1030 | — |
Frequently asked questions
Is Llama 3.2 1B better than o1-pro?
o1-pro is the stronger model overall, scoring 31.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3723× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or o1-pro?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
o1-pro does, with 200K tokens against 60K.
How many benchmarks do Llama 3.2 1B and o1-pro share?
0 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and o1-pro has 3.