# DeepSeek-V2 (MoE-236B, May 2024) vs Llama 3.1-8B

> Llama 3.1-8B has enough public results to be ranked (#347); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.

- Canonical page: https://noometry.com/compare/deepseek-v2-vs-llama-3-1-8b
- Last updated: 2026-10-10
- Shared benchmarks: 5

## Summary

- They share 5 benchmarks with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 1 category and Llama 3.1-8B in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V2 (MoE-236B, May 2024) leads 39.8 to 19.9.
- The biggest single-benchmark swing is BigCodeBench Complete: 59.4% for DeepSeek-V2 (MoE-236B, May 2024) and 40.5% for Llama 3.1-8B.

## Snapshot

| | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.7 | 22.8 |
| Rank | — | 347 |
| Context | — | 128K |
| Input $/M | — | $0.05 |
| Output $/M | — | $0.08 |
| Weights | Open | Open |

## Coding

- DeepSeek-V2 (MoE-236B, May 2024): 39.8 (#141)
- Llama 3.1-8B: 19.9 (#335)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| BigCodeBench Instruct | 48.9% | 32.8% |
| BigCodeBench Complete | 59.4% | 40.5% |
| SciCode | — | 13.2% |
| WeirdML | — | 1.7% |
| LMArena Coding | — | 1195 |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 22.3 (#123)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |

## Reasoning

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 14.5 (#316)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| Epoch Capabilities Index | 124.78 | 116.48 |
| PIQA | 83.9% | 81.2% |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1175 |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| BIG-Bench Hard | 78.8% | — |
| HellaSwag | 87.1% | — |
| WinoGrande | 86.3% | — |

## Math

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 9.5 (#311)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| Omni-MATH | — | 13.7% |
| LMArena Math | — | 1179 |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 8.0 (#302)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| MMLU | 78.4% | 56.1% |
| GPQA Diamond | — | 27% |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| LMArena Expert | — | 1144 |
| ARC (AI2) Challenge | 92.2% | — |
| BoolQ | — | 82.8% |
| TriviaQA | 80% | — |

## Multilingual

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 34.2 (#244)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | — | 1148 |
| LMArena Chinese | — | 1151 |
| LMArena French | — | 1176 |
| LMArena German | — | 1144 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1053 |
| LMArena Russian | — | 1158 |
| LMArena Spanish | — | 1169 |

## Instruction Following

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 59.1 (#253)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| IFEval | — | 74.3% |
| LMArena Instruction Following | — | 1159 |

## Long Context

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 35.8 (#233)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | — | 1182 |

## Writing & Preference

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.1-8B: 29.9 (#285)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.1-8B |
|---|---|---|
| LMArena Text | — | 1187 |
| LMArena Creative Writing | — | 1154 |
| EQ-Bench Creative Writing | — | 713 |
| WildBench | — | 68.7% |
| LMArena Multi-Turn | — | 1172 |

## FAQ

### Is DeepSeek-V2 (MoE-236B, May 2024) better than Llama 3.1-8B?

Llama 3.1-8B has enough public results to be ranked (#347); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.

### Is DeepSeek-V2 (MoE-236B, May 2024) or Llama 3.1-8B better for coding?

DeepSeek-V2 (MoE-236B, May 2024) scores higher on coding benchmarks: 39.8 versus 19.9 in the Noometry coding category.

### How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and Llama 3.1-8B share?

5 benchmarks have published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and Llama 3.1-8B has 43.