# DeepSeek V4 Flash vs Llama 4 Scout

> DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.7× less per token, which makes it the better buy when DeepSeek V4 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 28

## Summary

- They share 28 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Llama 4 Scout in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 9.1.
- The biggest single-benchmark swing is ARC-AGI-1: 89% for DeepSeek V4 Flash and 0.5% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4 Flash.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 53.6 | 27.7 |
| Rank | 35 | 330 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.10 |
| Output $/M | $0.60 | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Flash: 47.9 (#59)
- Llama 4 Scout: 20.2 (#339)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| SciCode | 49.9% | 17% |
| LMArena Coding | 1457 | 1286 |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1582 | — |
| WeirdML | 63% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 1,306 | — |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- Llama 4 Scout: 24.6 (#119)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#30)
- Llama 4 Scout: 9.1 (#345)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 61.4% | 0% |
| Kagi LLM Benchmark | 52.2% | 36.9% |
| ARC-AGI-1 | 89% | 0.5% |
| CritPt | 16.6% | 0% |
| LMArena Hard Prompts | 1444 | 1266 |
| DTBench | 90.9% | 57.9% |
| LMCA | 41.7% | 12% |
| Epoch Capabilities Index | 154.49 | 129.64 |
| SimpleBench | 61.1% | — |
| NYT Connections (extended) | 89.6% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |
| ForecastBench | — | 57.5 |

## Math

- DeepSeek V4 Flash: 60.3 (#37)
- Llama 4 Scout: 19.6 (#286)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.4% | 7.8% |
| LMArena Math | 1427 | 1287 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| ProofBench | 56% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- DeepSeek V4 Flash: 55.4 (#48)
- Llama 4 Scout: 31.9 (#217)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 91% | 51.8% |
| LMArena Expert | 1441 | 1235 |
| SimpleQA Verified | 33.6% | — |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- DeepSeek V4 Flash: —
- Llama 4 Scout: 32.2 (#102)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- DeepSeek V4 Flash: 53.0 (#72)
- Llama 4 Scout: 41.0 (#212)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1420 | 1252 |
| LMArena Chinese | 1468 | 1255 |
| LMArena French | 1439 | 1282 |
| LMArena German | 1418 | 1272 |
| LMArena Japanese | 1406 | 1206 |
| LMArena Korean | 1384 | 1207 |
| LMArena Russian | 1428 | 1263 |
| LMArena Spanish | 1436 | 1278 |

## Instruction Following

- DeepSeek V4 Flash: 74.9 (#81)
- Llama 4 Scout: 65.8 (#217)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1421 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- DeepSeek V4 Flash: 43.8 (#85)
- Llama 4 Scout: 27.5 (#294)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1434 | 1265 |
| Fiction.LiveBench | — | 36% |

## Writing & Preference

- DeepSeek V4 Flash: 63.8 (#61)
- Llama 4 Scout: 37.0 (#261)

| Benchmark | DeepSeek V4 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1432 | 1279 |
| LMArena Creative Writing | 1403 | 1249 |
| EQ-Bench Creative Writing | 1559 | 783 |
| LMArena Multi-Turn | 1449 | 1280 |
| WildBench | — | 78% |

## FAQ

### Is DeepSeek V4 Flash better than Llama 4 Scout?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.7× less per token, which makes it the better buy when DeepSeek V4 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4 Flash 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; DeepSeek V4 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4 Flash or Llama 4 Scout better for coding?

DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4 Flash does, with 1M tokens against 128K.

### How many benchmarks do DeepSeek V4 Flash and Llama 4 Scout share?

28 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Llama 4 Scout has 43.