# Llama 4 Scout vs o1-pro

> o1-pro is the stronger model overall, scoring 31.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 1750× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-4-scout-vs-o1-pro
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. Llama 4 Scout scores higher in 1 category and o1-pro in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where o1-pro leads 20.2 to 9.2.
- The biggest single-benchmark swing is ARC-AGI-1: 0.5% for Llama 4 Scout and 23.3% for o1-pro.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-pro accepts more context: 200K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.

## Snapshot

| | Llama 4 Scout | o1-pro |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 27.4 | 31.2 |
| Rank | 323 | 264 |
| Context | 128K | 200K |
| Input $/M | $0.10 | $150 |
| Output $/M | $0.30 | $600 |
| Weights | Open | Proprietary |

## Coding

- Llama 4 Scout: 19.9 (#334)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| SWE-bench Verified (bash only) | 9.1% | — |
| SciCode | 17% | — |
| LMArena Coding | 1286 | — |
| BigCodeBench Complete | 43.1% | — |

## Agentic & Tool Use

- Llama 4 Scout: 24.4 (#109)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |

## Reasoning

- Llama 4 Scout: 9.2 (#339)
- o1-pro: 20.2 (#223)

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| ARC-AGI-1 | 0.5% | 23.3% |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| CritPt | 0% | — |
| EnigmaEval | — | 6.1% |
| LMArena Hard Prompts | 1266 | — |
| DTBench | 57.9% | — |
| LMCA | 12% | — |
| Epoch Capabilities Index | 129.64 | — |
| ForecastBench | 57.5 | — |

## Math

- Llama 4 Scout: 18.4 (#280)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| Omni-MATH | 37.3% | — |
| LMArena Math | 1286 | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |

## Knowledge

- Llama 4 Scout: 31.8 (#208)
- o1-pro: 29.3 (#227)

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| GPQA Diamond | 51.8% | — |
| Humanity's Last Exam | — | 8.1% |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
| LMArena Expert | 1236 | — |

## Multimodal

- Llama 4 Scout: 31.6 (#98)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |

## Multilingual

- Llama 4 Scout: 41.3 (#207)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| LMArena Non-English | 1253 | — |
| LMArena Chinese | 1256 | — |
| LMArena French | 1283 | — |
| LMArena German | 1274 | — |
| LMArena Japanese | 1209 | — |
| LMArena Korean | 1207 | — |
| LMArena Russian | 1263 | — |
| LMArena Spanish | 1277 | — |

## Instruction Following

- Llama 4 Scout: 66.0 (#212)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| IFEval | 81.8% | — |
| LMArena Instruction Following | 1248 | — |

## Long Context

- Llama 4 Scout: 27.5 (#289)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| Fiction.LiveBench | 36% | — |
| LMArena Longer Query | 1265 | — |

## Writing & Preference

- Llama 4 Scout: 37.1 (#256)
- o1-pro: —

| Benchmark | Llama 4 Scout | o1-pro |
|---|---|---|
| LMArena Text | 1279 | — |
| LMArena Creative Writing | 1249 | — |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
| LMArena Multi-Turn | 1281 | — |

## FAQ

### Is Llama 4 Scout better than o1-pro?

o1-pro is the stronger model overall, scoring 31.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 1750× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.

### Which is cheaper, Llama 4 Scout or o1-pro?

Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 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 4 Scout and o1-pro share?

1 benchmark has published results for both models. Llama 4 Scout has 43 scored results on Noometry and o1-pro has 3.