# DeepSeek-V3 vs Llama 3.2 3B

> DeepSeek-V3 is the stronger model overall, scoring 39.2 to 28.4 on the Noometry Index. Llama 3.2 3B costs 3.4× 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-3-2-3b
- Last updated: 2026-10-10
- Shared benchmarks: 16

## Summary

- They share 16 benchmarks with published results for both. DeepSeek-V3 scores higher in 7 categories and Llama 3.2 3B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3 leads 57.6 to 24.9.
- The biggest single-benchmark swing is BigCodeBench Complete: 62.2% for DeepSeek-V3 and 28.3% for Llama 3.2 3B.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 131K.

## Snapshot

| | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.2 | 28.4 |
| Rank | 157 | 318 |
| Context | 164K | 131K |
| Input $/M | $0.24 | $0.05 |
| Output $/M | $0.90 | $0.33 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3: 41.9 (#99)
- Llama 3.2 3B: 27.1 (#314)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| BigCodeBench Instruct | 50% | 23.4% |
| LMArena Coding | 1368 | 1098 |
| BigCodeBench Complete | 62.2% | 28.3% |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| LiveBench Coding | 70.9% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Llama 3.2 3B: 19.9 (#135)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.0 (#226)
- Llama 3.2 3B: 20.1 (#225)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1095 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.95 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 30.4 (#208)
- Llama 3.2 3B: 29.9 (#213)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1373 | 1126 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#147)
- Llama 3.2 3B: 29.1 (#232)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1352 | 1090 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multilingual

- DeepSeek-V3: 48.8 (#138)
- Llama 3.2 3B: 26.4 (#276)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1358 | 1019 |
| LMArena Chinese | 1390 | 1017 |
| LMArena German | 1374 | 1056 |
| LMArena Russian | 1372 | 949 |
| LMArena French | 1386 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1321 | — |
| LMArena Spanish | 1358 | — |

## Instruction Following

- DeepSeek-V3: 73.0 (#125)
- Llama 3.2 3B: 56.3 (#270)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1345 | 1089 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#248)
- Llama 3.2 3B: 33.4 (#256)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1353 | 1101 |
| Fiction.LiveBench | 50% | — |

## Writing & Preference

- DeepSeek-V3: 57.6 (#124)
- Llama 3.2 3B: 24.9 (#302)

| Benchmark | DeepSeek-V3 | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1375 | 1110 |
| LMArena Creative Writing | 1364 | 1094 |
| EQ-Bench Creative Writing | 1472 | 595 |
| LMArena Multi-Turn | 1389 | 1105 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than Llama 3.2 3B?

DeepSeek-V3 is the stronger model overall, scoring 39.2 to 28.4 on the Noometry Index. Llama 3.2 3B costs 3.4× 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 3.2 3B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.

### Is DeepSeek-V3 or Llama 3.2 3B better for coding?

DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 27.1 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3 does, with 164K tokens against 131K.

### How many benchmarks do DeepSeek-V3 and Llama 3.2 3B share?

16 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Llama 3.2 3B has 18.