# DeepSeek-V3 vs Llama 4 Scout

> DeepSeek-V3 is the stronger model overall, scoring 39.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 2.7× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 37

## Summary

- They share 37 benchmarks with published results for both. DeepSeek-V3 scores higher in 8 categories and Llama 4 Scout in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3 leads 41.9 to 19.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.2 | 27.4 |
| Rank | 157 | 323 |
| Context | 164K | 128K |
| Input $/M | $0.24 | $0.10 |
| Output $/M | $0.90 | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3: 41.9 (#99)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| SciCode | 35.8% | 17% |
| LMArena Coding | 1368 | 1286 |
| BigCodeBench Complete | 62.2% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| Aider Polyglot | 55.1% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

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

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.0 (#226)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 36.9% |
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1365 | 1266 |
| DTBench | 64.8% | 57.9% |
| LMCA | 15.5% | 12% |
| Epoch Capabilities Index | 135.95 | 129.64 |
| ForecastBench | 59.1 | 57.5 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 27.2% | — |
| ARC-AGI-1 | — | 0.5% |
| LiveBench Reasoning | 65.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 30.4 (#208)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 7.8% |
| Omni-MATH | 40.3% | 37.3% |
| LMArena Math | 1373 | 1286 |
| MATH Level 5 | 75.5% | 62.3% |
| FrontierMath (Feb 2025 set) | 1.7% | 0% |
| LiveBench Math | 73.5% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#147)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 67.6% | 51.8% |
| MMLU-Pro | 72.3% | 74.2% |
| Vectara Hallucination Rate | 6.1% | 7.7% |
| GPQA (HELM) | 53.8% | 50.7% |
| LMArena Expert | 1352 | 1236 |
| Confabulations | 26.1% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

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

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

## Multilingual

- DeepSeek-V3: 48.8 (#138)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1358 | 1253 |
| LMArena Chinese | 1390 | 1256 |
| LMArena French | 1386 | 1283 |
| LMArena German | 1374 | 1274 |
| LMArena Japanese | 1333 | 1209 |
| LMArena Korean | 1321 | 1207 |
| LMArena Russian | 1372 | 1263 |
| LMArena Spanish | 1358 | 1277 |

## Instruction Following

- DeepSeek-V3: 73.0 (#125)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| IFEval | 83.2% | 81.8% |
| LMArena Instruction Following | 1345 | 1248 |
| LiveBench Instruction Following | 81.5% | — |

## Long Context

- DeepSeek-V3: 34.0 (#248)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 50% | 36% |
| LMArena Longer Query | 1353 | 1265 |

## Writing & Preference

- DeepSeek-V3: 57.6 (#124)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | DeepSeek-V3 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1375 | 1279 |
| LMArena Creative Writing | 1364 | 1249 |
| EQ-Bench Creative Writing | 1472 | 783 |
| WildBench | 83% | 78% |
| LMArena Multi-Turn | 1389 | 1281 |
| Short-Story Creative Writing | 77% | — |
| LiveBench Language | 49.1% | — |

## FAQ

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

DeepSeek-V3 is the stronger model overall, scoring 39.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 2.7× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 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 lists at $0.24 and $0.90.

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

DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

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

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

37 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Llama 4 Scout has 43.