# DeepSeek V4.1 Flash vs Llama 4 Scout

> DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 27.4 on the Noometry Index. Llama 4 Scout costs 1.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. DeepSeek V4.1 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 V4.1 Flash leads 46.2 to 9.2.
- The biggest single-benchmark swing is LMCA: 47% for DeepSeek V4.1 Flash and 12% 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 V4.1 Flash.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 50.3 | 27.4 |
| Rank | 40 | 323 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.10 |
| Output $/M | $0.60 | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.8 (#29)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| SciCode | 51.9% | 17% |
| LMArena Coding | 1511 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1620 | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 1,092 | — |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: —
- Llama 4 Scout: 24.4 (#109)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |

## Reasoning

- DeepSeek V4.1 Flash: 46.2 (#39)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| CritPt | 14.3% | 0% |
| LMArena Hard Prompts | 1485 | 1266 |
| DTBench | 89.9% | 57.9% |
| LMCA | 47% | 12% |
| Epoch Capabilities Index | 155.01 | 129.64 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | — | 0.5% |
| Surface Evolver Bench | 46.3% | — |
| ForecastBench | — | 57.5 |

## Math

- DeepSeek V4.1 Flash: 48.4 (#52)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1493 | 1286 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| ProofBench | 54% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- DeepSeek V4.1 Flash: 41.6 (#97)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1505 | 1236 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- DeepSeek V4.1 Flash: —
- Llama 4 Scout: 31.6 (#98)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- DeepSeek V4.1 Flash: 55.5 (#29)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1452 | 1253 |
| LMArena Chinese | 1506 | 1256 |
| LMArena French | 1448 | 1283 |
| LMArena Russian | 1473 | 1263 |
| LMArena Spanish | 1499 | 1277 |
| LMArena German | — | 1274 |
| LMArena Japanese | — | 1209 |
| LMArena Korean | — | 1207 |

## Instruction Following

- DeepSeek V4.1 Flash: 77.6 (#24)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1475 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#46)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1476 | 1265 |
| Fiction.LiveBench | — | 36% |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.8 (#43)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | DeepSeek V4.1 Flash | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1465 | 1279 |
| LMArena Creative Writing | 1436 | 1249 |
| EQ-Bench Creative Writing | 1540 | 783 |
| LMArena Multi-Turn | 1460 | 1281 |
| WildBench | — | 78% |

## FAQ

### Is DeepSeek V4.1 Flash better than Llama 4 Scout?

DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 27.4 on the Noometry Index. Llama 4 Scout costs 1.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4.1 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 V4.1 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4.1 Flash or Llama 4 Scout better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.8 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek V4.1 Flash and Llama 4 Scout share?

20 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and Llama 4 Scout has 43.