# DeepSeek-V3.1 vs Llama 3-70B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 28.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-3-70b
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama 3-70B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 36.1 to 12.0.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 54.2% for Llama 3-70B.

## Snapshot

| | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 28.5 |
| Rank | 106 | 317 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Llama 3-70B: 35.3 (#215)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Coding | 1417 | 1206 |
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 43.6% |
| BigCodeBench Complete | — | 54.5% |
| HumanEval+ | — | 72% |
| MBPP+ | — | 69% |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Llama 3-70B: 20.9 (#132)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| Cybench | — | 5% |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Llama 3-70B: 17.4 (#284)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 35.1% |
| LMArena Hard Prompts | 1417 | 1195 |
| DTBench | 82.7% | 54.2% |
| Epoch Capabilities Index | 139.92 | 122.91 |
| ForecastBench | 58 | 57.1 |
| SimpleBench | 40% | — |
| LMCA | 24.3% | — |
| WinoGrande | — | 83.5% |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Llama 3-70B: 12.0 (#299)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Math | 1420 | 1218 |
| OTIS Mock AIME 2024-2025 | — | 4.3% |
| MATH Level 5 | — | 22.6% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Llama 3-70B: 20.8 (#272)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Expert | 1403 | 1149 |
| GPQA Diamond | — | 40.6% |
| Vectara Hallucination Rate | 5.5% | — |
| MMLU | — | 79.3% |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Llama 3-70B: 33.8 (#246)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Non-English | 1400 | 1142 |
| LMArena Chinese | 1470 | 1114 |
| LMArena French | 1447 | 1231 |
| LMArena German | 1412 | 1169 |
| LMArena Japanese | 1374 | 1017 |
| LMArena Korean | 1337 | 1017 |
| LMArena Russian | 1405 | 1159 |
| LMArena Spanish | 1429 | 1241 |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Llama 3-70B: 62.8 (#233)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1194 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Llama 3-70B: 35.6 (#235)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Longer Query | 1422 | 1174 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Llama 3-70B: 43.0 (#225)

| Benchmark | DeepSeek-V3.1 | Llama 3-70B |
|---|---|---|
| LMArena Text | 1420 | 1221 |
| LMArena Creative Writing | 1401 | 1210 |
| LMArena Multi-Turn | 1408 | 1223 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Llama 3-70B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 28.5 on the Noometry Index.

### Is DeepSeek-V3.1 or Llama 3-70B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 35.3 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Llama 3-70B share?

21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 3-70B has 31.