# DeepSeek-V3.1 vs Llama 4 Scout

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 2.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama 4 Scout in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 37.1.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 57.9% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 27.4 |
| Rank | 106 | 323 |
| Context | 164K | 128K |
| Input $/M | $0.25 | $0.10 |
| Output $/M | $0.95 | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1417 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| WeirdML | 38.4% | — |
| BigCodeBench Complete | — | 43.1% |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Llama 4 Scout: 24.4 (#109)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 36.9% |
| LMArena Hard Prompts | 1417 | 1266 |
| DTBench | 82.7% | 57.9% |
| LMCA | 24.3% | 12% |
| Epoch Capabilities Index | 139.92 | 129.64 |
| ForecastBench | 58 | 57.5 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 40% | — |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1420 | 1286 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 7.7% |
| LMArena Expert | 1403 | 1236 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- DeepSeek-V3.1: —
- Llama 4 Scout: 31.6 (#98)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1400 | 1253 |
| LMArena Chinese | 1470 | 1256 |
| LMArena French | 1447 | 1283 |
| LMArena German | 1412 | 1274 |
| LMArena Japanese | 1374 | 1209 |
| LMArena Korean | 1337 | 1207 |
| LMArena Russian | 1405 | 1263 |
| LMArena Spanish | 1429 | 1277 |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1400 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 52.8% | 36% |
| LMArena Longer Query | 1422 | 1265 |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | DeepSeek-V3.1 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1420 | 1279 |
| LMArena Creative Writing | 1401 | 1249 |
| EQ-Bench Creative Writing | 1436 | 783 |
| LMArena Multi-Turn | 1408 | 1281 |
| WildBench | — | 78% |

## FAQ

### Is DeepSeek-V3.1 better than Llama 4 Scout?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 2.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.

### Is DeepSeek-V3.1 or Llama 4 Scout better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1 and Llama 4 Scout share?

25 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 4 Scout has 43.