# DeepSeek-V2.5 (Sep 2024) vs Llama 3-8B

> DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 25.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v2-5-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 8 categories and Llama 3-8B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V2.5 (Sep 2024) leads 35.9 to 8.8.
- The biggest single-benchmark swing is BigCodeBench Instruct: 48.6% for DeepSeek-V2.5 (Sep 2024) and 31.9% for Llama 3-8B.

## Snapshot

| | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 37.6 | 25.5 |
| Rank | 200 | 344 |
| Context | — | — |
| Input $/M | — | — |
| Output $/M | — | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V2.5 (Sep 2024): 31.7 (#281)
- Llama 3-8B: 31.0 (#289)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| BigCodeBench Instruct | 48.6% | 31.9% |
| LMArena Coding | 1309 | 1152 |
| BigCodeBench Complete | 53.2% | 36.9% |
| HumanEval+ | 83.5% | 56.7% |
| MBPP+ | 74.1% | 54.8% |
| Aider Polyglot | 17.8% | — |

## Reasoning

- DeepSeek-V2.5 (Sep 2024): 25.6 (#145)
- Llama 3-8B: 14.3 (#326)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1133 |
| Chess Puzzles | — | 0% |
| DTBench | — | 43.9% |
| Adversarial NLI | — | 57.3% |
| Epoch Capabilities Index | — | 116.45 |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- DeepSeek-V2.5 (Sep 2024): 35.9 (#177)
- Llama 3-8B: 8.8 (#323)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Math | 1288 | 1151 |
| OTIS Mock AIME 2024-2025 | — | 1.9% |
| MATH Level 5 | — | 6.1% |

## Knowledge

- DeepSeek-V2.5 (Sep 2024): 34.8 (#193)
- Llama 3-8B: 7.8 (#308)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Expert | 1266 | 1113 |
| GPQA Diamond | — | 26.1% |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- DeepSeek-V2.5 (Sep 2024): 42.5 (#193)
- Llama 3-8B: 30.8 (#261)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1273 | 1098 |
| LMArena Chinese | 1318 | 1076 |
| LMArena French | 1289 | 1159 |
| LMArena German | 1258 | 1104 |
| LMArena Japanese | 1228 | 967 |
| LMArena Korean | 1209 | 1004 |
| LMArena Russian | 1289 | 1109 |
| LMArena Spanish | 1248 | 1173 |

## Instruction Following

- DeepSeek-V2.5 (Sep 2024): 67.5 (#194)
- Llama 3-8B: 58.4 (#260)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1280 | 1127 |

## Long Context

- DeepSeek-V2.5 (Sep 2024): 39.5 (#174)
- Llama 3-8B: 34.2 (#251)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1301 | 1128 |

## Writing & Preference

- DeepSeek-V2.5 (Sep 2024): 49.8 (#187)
- Llama 3-8B: 37.5 (#256)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | Llama 3-8B |
|---|---|---|
| LMArena Text | 1294 | 1166 |
| LMArena Creative Writing | 1285 | 1150 |
| LMArena Multi-Turn | 1297 | 1152 |

## FAQ

### Is DeepSeek-V2.5 (Sep 2024) better than Llama 3-8B?

DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 25.5 on the Noometry Index.

### Is DeepSeek-V2.5 (Sep 2024) or Llama 3-8B better for coding?

They score almost the same on coding (31.7 vs 31.0); test both on your own repository before choosing.

### How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Llama 3-8B share?

21 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Llama 3-8B has 34.