# DeepSeek-V2.5 (Sep 2024) vs Llama 4 Scout

> DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.0 to 27.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v2-5-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) 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-V2.5 (Sep 2024) leads 24.5 to 9.2.
- The biggest single-benchmark swing is BigCodeBench Complete: 53.2% for DeepSeek-V2.5 (Sep 2024) and 43.1% for Llama 4 Scout.

## Snapshot

| | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 37.0 | 27.4 |
| Rank | 197 | 323 |
| Context | — | 128K |
| Input $/M | — | $0.10 |
| Output $/M | — | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek-V2.5 (Sep 2024): 31.3 (#274)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1310 | 1286 |
| BigCodeBench Complete | 53.2% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| Aider Polyglot | 17.8% | — |
| SciCode | — | 17% |
| BigCodeBench Instruct | 48.6% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |

## Agentic & Tool Use

- DeepSeek-V2.5 (Sep 2024): —
- Llama 4 Scout: 24.4 (#109)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |

## Reasoning

- DeepSeek-V2.5 (Sep 2024): 24.5 (#145)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1266 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |

## Math

- DeepSeek-V2.5 (Sep 2024): 33.3 (#177)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1289 | 1286 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- DeepSeek-V2.5 (Sep 2024): 34.2 (#190)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1266 | 1236 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- DeepSeek-V2.5 (Sep 2024): —
- Llama 4 Scout: 31.6 (#98)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- DeepSeek-V2.5 (Sep 2024): 42.7 (#188)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1273 | 1253 |
| LMArena Chinese | 1317 | 1256 |
| LMArena French | 1288 | 1283 |
| LMArena German | 1258 | 1274 |
| LMArena Japanese | 1228 | 1209 |
| LMArena Korean | 1209 | 1207 |
| LMArena Russian | 1289 | 1263 |
| LMArena Spanish | 1248 | 1277 |

## Instruction Following

- DeepSeek-V2.5 (Sep 2024): 67.7 (#189)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1280 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- DeepSeek-V2.5 (Sep 2024): 39.5 (#169)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1301 | 1265 |
| Fiction.LiveBench | — | 36% |

## Writing & Preference

- DeepSeek-V2.5 (Sep 2024): 50.0 (#182)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1294 | 1279 |
| LMArena Creative Writing | 1285 | 1249 |
| LMArena Multi-Turn | 1297 | 1281 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |

## FAQ

### Is DeepSeek-V2.5 (Sep 2024) better than Llama 4 Scout?

DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.0 to 27.4 on the Noometry Index.

### Is DeepSeek-V2.5 (Sep 2024) or Llama 4 Scout better for coding?

DeepSeek-V2.5 (Sep 2024) scores higher on coding benchmarks: 31.3 versus 19.9 in the Noometry coding category.

### How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Llama 4 Scout share?

18 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Llama 4 Scout has 43.