# Llama-3.3-70B-Instruct vs o3-pro

> o3-pro is the stronger model overall, scoring 42.9 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 226× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-o3-pro
- Last updated: 2026-10-11
- Shared benchmarks: 7

## Summary

- They share 7 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and o3-pro in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 26.4.
- The biggest single-benchmark swing is Fiction.LiveBench: 33.3% for Llama-3.3-70B-Instruct and 97.2% for o3-pro.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 30.6 | 42.9 |
| Rank | 291 | 105 |
| Context | 128K | 200K |
| Input $/M | $0.10 | $20 |
| Output $/M | $0.32 | $80 |
| Weights | Open | Proprietary |

## Coding

- Llama-3.3-70B-Instruct: 31.0 (#290)
- o3-pro: 55.5 (#24)

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| WeirdML | 14.4% | 58.2% |
| Aider Polyglot | — | 84.9% |
| SciCode | 26% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 57.5% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.8 (#105)
- o3-pro: —

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 14.1 (#327)
- o3-pro: 23.8 (#171)

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| DTBench | 59.5% | 86.9% |
| LMCA | 17.5% | 38.5% |
| Epoch Capabilities Index | 127.33 | 147.42 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 72.1% |
| ARC-AGI-1 | — | 59.3% |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| LMArena Hard Prompts | 1257 | — |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 15.3 (#298)
- o3-pro: —

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1267 | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.6 (#226)
- o3-pro: 29.5 (#238)

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| Confabulations | 22.8% | 14.2% |
| Vectara Hallucination Rate | 4.1% | 23.3% |
| GPQA Diamond | 47.4% | — |
| LMArena Expert | 1225 | — |
| MMLU | 86.3% | — |

## Multilingual

- Llama-3.3-70B-Instruct: 39.9 (#220)
- o3-pro: —

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| LMArena Non-English | 1236 | — |
| LMArena Chinese | 1217 | — |
| LMArena French | 1281 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1150 | — |
| LMArena Korean | 1143 | — |
| LMArena Russian | 1252 | — |
| LMArena Spanish | 1270 | — |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.1 (#157)
- o3-pro: —

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| LiveBench Instruction Following | 82.7% | — |
| LMArena Instruction Following | 1242 | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#295)
- o3-pro: 72.2 (#1)

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| Fiction.LiveBench | 33.3% | 97.2% |
| LMArena Longer Query | 1256 | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.6 (#207)
- o3-pro: 57.1 (#133)

| Benchmark | Llama-3.3-70B-Instruct | o3-pro |
|---|---|---|
| LMArena Text | 1274 | — |
| LMArena Creative Writing | 1250 | — |
| Short-Story Creative Writing | — | 84.4% |
| LMArena Multi-Turn | 1280 | — |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than o3-pro?

o3-pro is the stronger model overall, scoring 42.9 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 226× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

### Which is cheaper, Llama-3.3-70B-Instruct or o3-pro?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; o3-pro lists at $20 and $80.

### Is Llama-3.3-70B-Instruct or o3-pro better for coding?

o3-pro scores higher on coding benchmarks: 55.5 versus 31.0 in the Noometry coding category.

### Which has the bigger context window?

o3-pro does, with 200K tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and o3-pro share?

7 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and o3-pro has 12.