Model comparison
Llama-3.3-70B-Instruct vs o1-pro
Llama-3.3-70B-Instruct and o1-pro score almost the same on the Noometry Index (30.3 vs 31.2), so choose on price, context window or the category you care about most.
Last verified . 0 shared benchmarks.
MetaSummary
- The widest gap is in reasoning, where o1-pro leads 20.2 to 13.7.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-pro accepts more context: 200K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | o1-pro | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 30.3 | 31.2 |
| Released | 2024-12-06 | 2025-03-19 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.10 | $150 |
| Output $ / M tokens | $0.32 | $600 |
| Results tracked | 43 | 3 |
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Category by category
Coding Not comparable
Llama-3.3-70B-Instruct: 30.6 (#284), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.6 (#100), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning o1-pro leads
Llama-3.3-70B-Instruct: 13.7 (#325), o1-pro: 20.2 (#223)
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| SimpleBench | 19.9% | — |
| ARC-AGI-1 | — | 23.3% |
| CritPt | 0% | — |
| EnigmaEval | — | 6.1% |
| LiveBench Reasoning | 50.8% | — |
| LMArena Hard Prompts | 1257 | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.32 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Not comparable
Llama-3.3-70B-Instruct: 14.3 (#292), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1268 | — |
| MATH Level 5 | 41.6% | — |
Knowledge Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 30.5 (#220), o1-pro: 29.3 (#227)
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| GPQA Diamond | 47.4% | — |
| Humanity's Last Exam | — | 8.1% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| LMArena Expert | 1225 | — |
| MMLU | 86.3% | — |
Multilingual Not comparable
Llama-3.3-70B-Instruct: 40.1 (#215), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| LMArena Non-English | 1236 | — |
| LMArena Chinese | 1217 | — |
| LMArena French | 1280 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1149 | — |
| LMArena Korean | 1142 | — |
| LMArena Russian | 1252 | — |
| LMArena Spanish | 1269 | — |
Instruction Following Not comparable
Llama-3.3-70B-Instruct: 71.3 (#152), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| LiveBench Instruction Following | 82.7% | — |
| LMArena Instruction Following | 1242 | — |
Long Context Not comparable
Llama-3.3-70B-Instruct: 26.4 (#290), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| Fiction.LiveBench | 33.3% | — |
| LMArena Longer Query | 1256 | — |
Writing & Preference Not comparable
Llama-3.3-70B-Instruct: 47.8 (#203), o1-pro: —
| Benchmark | Llama-3.3-70B-Instruct | o1-pro |
|---|---|---|
| LMArena Text | 1274 | — |
| LMArena Creative Writing | 1250 | — |
| LMArena Multi-Turn | 1279 | — |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than o1-pro?
Llama-3.3-70B-Instruct and o1-pro score almost the same on the Noometry Index (30.3 vs 31.2), so choose on price, context window or the category you care about most.
Which is cheaper, Llama-3.3-70B-Instruct or o1-pro?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 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-3.3-70B-Instruct and o1-pro share?
0 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and o1-pro has 3.