# Llama 4 Scout vs Mistral Large 3

> Mistral Large 3 is the stronger model overall, scoring 38.6 to 27.4 on the Noometry Index. Llama 4 Scout costs 2.5× less per token, which makes it the better buy when Mistral Large 3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-4-scout-vs-mistral-large-3
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and Mistral Large 3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Mistral Large 3 leads 60.2 to 37.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 36.9% for Llama 4 Scout and 50.9% for Mistral Large 3.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.25 / $0.75 for Mistral Large 3.
- Mistral Large 3 accepts more context: 262K tokens versus 128K.

## Snapshot

| | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 27.4 | 38.6 |
| Rank | 323 | 172 |
| Context | 128K | 262K |
| Input $/M | $0.10 | $0.25 |
| Output $/M | $0.30 | $0.75 |
| Weights | Open | Open |

## Coding

- Llama 4 Scout: 19.9 (#334)
- Mistral Large 3: 34.1 (#230)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Coding | 1286 | 1447 |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena WebDev | — | 1230 |
| SciCode | 17% | — |
| BigCodeBench Complete | 43.1% | — |

## Agentic & Tool Use

- Llama 4 Scout: 24.4 (#109)
- Mistral Large 3: —

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |

## Reasoning

- Llama 4 Scout: 9.2 (#339)
- Mistral Large 3: 14.6 (#314)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| Kagi LLM Benchmark | 36.9% | 50.9% |
| LMArena Hard Prompts | 1266 | 1429 |
| ARC-AGI-2 | 0% | — |
| NYT Connections (extended) | — | 7.5% |
| ARC-AGI-1 | 0.5% | — |
| CritPt | 0% | — |
| Thematic Generalization | — | 23% |
| DTBench | 57.9% | — |
| LMCA | 12% | — |
| Epoch Capabilities Index | 129.64 | — |
| ForecastBench | 57.5 | — |

## Math

- Llama 4 Scout: 18.4 (#280)
- Mistral Large 3: 36.0 (#131)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1286 | 1415 |
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| Omni-MATH | 37.3% | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |

## Knowledge

- Llama 4 Scout: 31.8 (#208)
- Mistral Large 3: 35.5 (#173)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| Vectara Hallucination Rate | 7.7% | 14.5% |
| LMArena Expert | 1236 | 1421 |
| GPQA Diamond | 51.8% | — |
| MMLU-Pro | 74.2% | — |
| GPQA (HELM) | 50.7% | — |

## Multimodal

- Llama 4 Scout: 31.6 (#98)
- Mistral Large 3: 37.6 (#65)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Vision | 1118 | 1222 |
| SpatialViz-Bench | 34.2% | — |

## Multilingual

- Llama 4 Scout: 41.3 (#207)
- Mistral Large 3: 52.6 (#79)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1253 | 1412 |
| LMArena Chinese | 1256 | 1448 |
| LMArena French | 1283 | 1456 |
| LMArena German | 1274 | 1438 |
| LMArena Japanese | 1209 | 1399 |
| LMArena Korean | 1207 | 1384 |
| LMArena Russian | 1263 | 1412 |
| LMArena Spanish | 1277 | 1437 |

## Instruction Following

- Llama 4 Scout: 66.0 (#212)
- Mistral Large 3: 74.1 (#104)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1248 | 1402 |
| IFEval | 81.8% | — |

## Long Context

- Llama 4 Scout: 27.5 (#289)
- Mistral Large 3: 43.1 (#102)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1265 | 1413 |
| Fiction.LiveBench | 36% | — |

## Writing & Preference

- Llama 4 Scout: 37.1 (#256)
- Mistral Large 3: 60.2 (#100)

| Benchmark | Llama 4 Scout | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1279 | 1427 |
| LMArena Creative Writing | 1249 | 1385 |
| EQ-Bench Creative Writing | 783 | 1412 |
| LMArena Multi-Turn | 1281 | 1429 |
| WildBench | 78% | — |

## FAQ

### Is Llama 4 Scout better than Mistral Large 3?

Mistral Large 3 is the stronger model overall, scoring 38.6 to 27.4 on the Noometry Index. Llama 4 Scout costs 2.5× less per token, which makes it the better buy when Mistral Large 3's lead doesn't matter for your workload.

### Which is cheaper, Llama 4 Scout or Mistral Large 3?

Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Mistral Large 3 lists at $0.25 and $0.75.

### Is Llama 4 Scout or Mistral Large 3 better for coding?

Mistral Large 3 scores higher on coding benchmarks: 34.1 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Mistral Large 3 does, with 262K tokens against 128K.

### How many benchmarks do Llama 4 Scout and Mistral Large 3 share?

21 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Mistral Large 3 has 24.