# DeepSeek-V2 (MoE-236B, May 2024) vs Llama 3.2 3B

> Llama 3.2 3B has enough public results to be ranked (#318); 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-3-2-3b
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 1 category and Llama 3.2 3B 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 27.1.
- The biggest single-benchmark swing is BigCodeBench Complete: 59.4% for DeepSeek-V2 (MoE-236B, May 2024) and 28.3% for Llama 3.2 3B.

## Snapshot

| | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.7 | 28.4 |
| Rank | — | 318 |
| Context | — | 131K |
| Input $/M | — | $0.05 |
| Output $/M | — | $0.33 |
| Weights | Open | Open |

## Coding

- DeepSeek-V2 (MoE-236B, May 2024): 39.8 (#141)
- Llama 3.2 3B: 27.1 (#314)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| BigCodeBench Instruct | 48.9% | 23.4% |
| BigCodeBench Complete | 59.4% | 28.3% |
| LMArena Coding | — | 1098 |

## Agentic & Tool Use

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 19.9 (#135)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |

## Reasoning

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 20.1 (#225)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | — | 1095 |
| BIG-Bench Hard | 78.8% | — |
| Epoch Capabilities Index | 124.78 | — |
| HellaSwag | 87.1% | — |
| PIQA | 83.9% | — |
| WinoGrande | 86.3% | — |

## Math

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 29.9 (#213)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Math | — | 1126 |

## Knowledge

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 29.1 (#232)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | — | 1090 |
| ARC (AI2) Challenge | 92.2% | — |
| MMLU | 78.4% | — |
| TriviaQA | 80% | — |

## Multilingual

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 26.4 (#276)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | — | 1019 |
| LMArena Chinese | — | 1017 |
| LMArena German | — | 1056 |
| LMArena Russian | — | 949 |

## Instruction Following

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 56.3 (#270)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | — | 1089 |

## Long Context

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 33.4 (#256)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | — | 1101 |

## Writing & Preference

- DeepSeek-V2 (MoE-236B, May 2024): —
- Llama 3.2 3B: 24.9 (#302)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Llama 3.2 3B |
|---|---|---|
| LMArena Text | — | 1110 |
| LMArena Creative Writing | — | 1094 |
| EQ-Bench Creative Writing | — | 595 |
| LMArena Multi-Turn | — | 1105 |

## FAQ

### Is DeepSeek-V2 (MoE-236B, May 2024) better than Llama 3.2 3B?

Llama 3.2 3B has enough public results to be ranked (#318); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.

### Is DeepSeek-V2 (MoE-236B, May 2024) or Llama 3.2 3B better for coding?

DeepSeek-V2 (MoE-236B, May 2024) scores higher on coding benchmarks: 39.8 versus 27.1 in the Noometry coding category.

### How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and Llama 3.2 3B share?

2 benchmarks have published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and Llama 3.2 3B has 18.