# Llama-3.3-70B-Instruct vs Llama 4 Scout

> Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.3 to 27.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 30

## Summary

- They share 30 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 5 categories and Llama 4 Scout in 4 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where Llama-3.3-70B-Instruct leads 30.6 to 19.9.
- The biggest single-benchmark swing is MATH Level 5: 41.6% for Llama-3.3-70B-Instruct and 62.3% for Llama 4 Scout.
- Both cost about the same: $0.10 input and $0.32 output per million tokens.

## Snapshot

| | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 30.3 | 27.4 |
| Rank | 283 | 323 |
| Context | 128K | 128K |
| Input $/M | $0.10 | $0.10 |
| Output $/M | $0.32 | $0.30 |
| Weights | Open | Open |

## Coding

- Llama-3.3-70B-Instruct: 30.6 (#284)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| SciCode | 26% | 17% |
| LMArena Coding | 1268 | 1286 |
| BigCodeBench Complete | 57.5% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.6 (#100)
- Llama 4 Scout: 24.4 (#109)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | 28.1% |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 13.7 (#325)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1257 | 1266 |
| DTBench | 59.5% | 57.9% |
| LMCA | 17.5% | 12% |
| Epoch Capabilities Index | 127.32 | 129.64 |
| ForecastBench | 58.6 | 57.5 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| LiveBench Reasoning | 50.8% | — |
| LiveBench Data Analysis | 49.5% | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 14.3 (#292)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 7.8% |
| LMArena Math | 1268 | 1286 |
| MATH Level 5 | 41.6% | 62.3% |
| Omni-MATH | — | 37.3% |
| LiveBench Math | 42.2% | — |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- Llama-3.3-70B-Instruct: 30.5 (#220)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 47.4% | 51.8% |
| Vectara Hallucination Rate | 4.1% | 7.7% |
| LMArena Expert | 1225 | 1236 |
| MMLU-Pro | — | 74.2% |
| Confabulations | 22.8% | — |
| GPQA (HELM) | — | 50.7% |
| MMLU | 86.3% | — |

## Multimodal

- Llama-3.3-70B-Instruct: —
- Llama 4 Scout: 31.6 (#98)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- Llama-3.3-70B-Instruct: 40.1 (#215)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1236 | 1253 |
| LMArena Chinese | 1217 | 1256 |
| LMArena French | 1280 | 1283 |
| LMArena German | 1251 | 1274 |
| LMArena Japanese | 1149 | 1209 |
| LMArena Korean | 1142 | 1207 |
| LMArena Russian | 1252 | 1263 |
| LMArena Spanish | 1269 | 1277 |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.3 (#152)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1242 | 1248 |
| LiveBench Instruction Following | 82.7% | — |
| IFEval | — | 81.8% |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#290)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 33.3% | 36% |
| LMArena Longer Query | 1256 | 1265 |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.8 (#203)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | Llama-3.3-70B-Instruct | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1274 | 1279 |
| LMArena Creative Writing | 1250 | 1249 |
| LMArena Multi-Turn | 1279 | 1281 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than Llama 4 Scout?

Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.3 to 27.4 on the Noometry Index.

### Which is cheaper, Llama-3.3-70B-Instruct or Llama 4 Scout?

Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.

### Is Llama-3.3-70B-Instruct or Llama 4 Scout better for coding?

Llama-3.3-70B-Instruct scores higher on coding benchmarks: 30.6 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Both accept 128K tokens.

### How many benchmarks do Llama-3.3-70B-Instruct and Llama 4 Scout share?

30 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Llama 4 Scout has 43.