Model comparison
Llama 3.1-70B vs o1-pro
o1-pro is the stronger model overall, scoring 31.2 to 29.4 on the Noometry Index. Llama 3.1-70B costs 656× 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 29.3 to 24.1.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-pro accepts more context: 200K tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-70B | o1-pro | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 29.4 | 31.2 |
| Released | 2024-07-23 | 2025-03-19 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.40 | $150 |
| Output $ / M tokens | $0.40 | $600 |
| Results tracked | 35 | 3 |
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Category by category
Coding Not comparable
Llama 3.1-70B: 29.8 (#292), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| WeirdML | 9% | — |
| BigCodeBench Instruct | 46.1% | — |
| LMArena Coding | 1260 | — |
| BigCodeBench Complete | 54.8% | — |
Agentic & Tool Use Not comparable
Llama 3.1-70B: 24.9 (#104), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
Reasoning Too close to call
Llama 3.1-70B: 20.8 (#215), o1-pro: 20.2 (#223)
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| ARC-AGI-1 | — | 23.3% |
| EnigmaEval | — | 6.1% |
| LMArena Hard Prompts | 1241 | — |
| DTBench | 60% | — |
| LMCA | 14.8% | — |
| Epoch Capabilities Index | 125.91 | — |
Math Not comparable
Llama 3.1-70B: 12.7 (#298), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | — |
| Omni-MATH | 21% | — |
| LMArena Math | 1252 | — |
| MATH Level 5 | 36.7% | — |
Knowledge o1-pro leads
Llama 3.1-70B: 24.1 (#264), o1-pro: 29.3 (#227)
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| GPQA Diamond | 44.2% | — |
| Humanity's Last Exam | — | 8.1% |
| MMLU-Pro | 65.3% | — |
| GPQA (HELM) | 42.6% | — |
| LMArena Expert | 1209 | — |
| MMLU | 80.1% | — |
Multilingual Not comparable
Llama 3.1-70B: 38.9 (#220), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| LMArena Non-English | 1219 | — |
| LMArena Chinese | 1215 | — |
| LMArena French | 1260 | — |
| LMArena German | 1222 | — |
| LMArena Japanese | 1132 | — |
| LMArena Korean | 1140 | — |
| LMArena Russian | 1234 | — |
| LMArena Spanish | 1253 | — |
Instruction Following Not comparable
Llama 3.1-70B: 65.5 (#218), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| IFEval | 82.1% | — |
| LMArena Instruction Following | 1231 | — |
Long Context Not comparable
Llama 3.1-70B: 37.6 (#208), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| LMArena Longer Query | 1241 | — |
Writing & Preference Not comparable
Llama 3.1-70B: 35.6 (#262), o1-pro: —
| Benchmark | Llama 3.1-70B | o1-pro |
|---|---|---|
| LMArena Text | 1261 | — |
| LMArena Creative Writing | 1232 | — |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
| LMArena Multi-Turn | 1256 | — |
Frequently asked questions
Is Llama 3.1-70B better than o1-pro?
o1-pro is the stronger model overall, scoring 31.2 to 29.4 on the Noometry Index. Llama 3.1-70B costs 656× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-70B or o1-pro?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
o1-pro does, with 200K tokens against 128K.
How many benchmarks do Llama 3.1-70B and o1-pro share?
0 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and o1-pro has 3.