Model comparison
Llama 2-70B vs o3-pro
o3-pro is the stronger model overall, scoring 42.9 to 24.4 on the Noometry Index.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. Llama 2-70B 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 32.3.
- The biggest single-benchmark swing is DTBench: 41.6% for Llama 2-70B and 86.9% for o3-pro.
- Llama 2-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 2-70B | o3-pro | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 24.4 | 42.9 |
| Released | 2023-07-18 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | — | 200K |
| Max output | — | 100K |
| Input $ / M tokens | — | $20 |
| Output $ / M tokens | — | $80 |
| Results tracked | 35 | 12 |
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Category by category
Coding o3-pro leads
Llama 2-70B: 31.4 (#286), o3-pro: 55.5 (#24)
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| Aider Polyglot | — | 84.9% |
| WeirdML | — | 58.2% |
| LMArena Coding | 1079 | — |
Reasoning o3-pro leads
Llama 2-70B: 14.4 (#325), o3-pro: 23.8 (#171)
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| DTBench | 41.6% | 86.9% |
| Epoch Capabilities Index | 113.79 | 147.42 |
| ARC-AGI-2 | — | 4.9% |
| Kagi LLM Benchmark | — | 72.1% |
| ARC-AGI-1 | — | 59.3% |
| LMArena Hard Prompts | 1073 | — |
| LMCA | — | 38.5% |
| BIG-Bench Hard | 64.9% | — |
| CommonsenseQA 2.0 | 50% | — |
| ForecastBench | 51.4 | — |
| HellaSwag | 85.3% | — |
| LAMBADA | 78.9% | — |
| PIQA | 82.8% | — |
| WinoGrande | 80.2% | — |
Math Not comparable
Llama 2-70B: 8.1 (#326), o3-pro: —
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0% | — |
| LMArena Math | 1091 | — |
| MATH Level 5 | 3.3% | — |
| GSM8K | 69.6% | — |
Knowledge o3-pro leads
Llama 2-70B: 7.4 (#310), o3-pro: 29.5 (#238)
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| GPQA Diamond | 26.3% | — |
| Confabulations | — | 14.2% |
| Vectara Hallucination Rate | — | 23.3% |
| LMArena Expert | 1039 | — |
| ARC (AI2) Challenge | 78.3% | — |
| BoolQ | 88.6% | — |
| MMLU | 69.9% | — |
| OpenBookQA | 60.2% | — |
| TriviaQA | 87.6% | — |
Multilingual Not comparable
Llama 2-70B: 27.7 (#274), o3-pro: —
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| LMArena Non-English | 1045 | — |
| LMArena Chinese | 995 | — |
| LMArena French | 1090 | — |
| LMArena German | 1041 | — |
| LMArena Japanese | 927 | — |
| LMArena Korean | 964 | — |
| LMArena Russian | 1083 | — |
| LMArena Spanish | 1143 | — |
Instruction Following Not comparable
Llama 2-70B: 54.9 (#278), o3-pro: —
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| LMArena Instruction Following | 1071 | — |
Long Context o3-pro leads
Llama 2-70B: 32.3 (#270), o3-pro: 72.2 (#1)
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| LMArena Longer Query | 1062 | — |
Writing & Preference o3-pro leads
Llama 2-70B: 32.3 (#279), o3-pro: 57.1 (#133)
| Benchmark | Llama 2-70B | o3-pro |
|---|---|---|
| LMArena Text | 1115 | — |
| LMArena Creative Writing | 1075 | — |
| Short-Story Creative Writing | — | 84.4% |
| LMArena Multi-Turn | 1088 | — |
Frequently asked questions
Is Llama 2-70B better than o3-pro?
o3-pro is the stronger model overall, scoring 42.9 to 24.4 on the Noometry Index.
Is Llama 2-70B or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 31.4 in the Noometry coding category.
How many benchmarks do Llama 2-70B and o3-pro share?
2 benchmarks have published results for both models. Llama 2-70B has 35 scored results on Noometry and o3-pro has 12.