# DeepSeek-V3.1 vs Llama 2-70B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 24.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-2-70b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama 2-70B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.0 to 7.4.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 41.6% for Llama 2-70B.

## Snapshot

| | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 24.1 |
| Rank | 106 | 343 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Llama 2-70B: 30.8 (#281)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Coding | 1417 | 1079 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Llama 2-70B: 13.7 (#324)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1073 |
| DTBench | 82.7% | 41.6% |
| Epoch Capabilities Index | 139.92 | 113.59 |
| ForecastBench | 58 | 51.4 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMCA | 24.3% | — |
| BIG-Bench Hard | — | 64.9% |
| CommonsenseQA 2.0 | — | 50% |
| HellaSwag | — | 85.3% |
| LAMBADA | — | 78.9% |
| PIQA | — | 82.8% |
| WinoGrande | — | 80.2% |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Llama 2-70B: 7.5 (#320)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Math | 1420 | 1091 |
| OTIS Mock AIME 2024-2025 | — | 0% |
| MATH Level 5 | — | 3.3% |
| GSM8K | — | 69.6% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Llama 2-70B: 7.4 (#305)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Expert | 1403 | 1039 |
| GPQA Diamond | — | 26.3% |
| Vectara Hallucination Rate | 5.5% | — |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 88.6% |
| MMLU | — | 69.9% |
| OpenBookQA | — | 60.2% |
| TriviaQA | — | 87.6% |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Llama 2-70B: 27.9 (#269)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Non-English | 1400 | 1045 |
| LMArena Chinese | 1470 | 994 |
| LMArena French | 1447 | 1089 |
| LMArena German | 1412 | 1041 |
| LMArena Japanese | 1374 | 927 |
| LMArena Korean | 1337 | 964 |
| LMArena Russian | 1405 | 1083 |
| LMArena Spanish | 1429 | 1142 |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Llama 2-70B: 55.1 (#273)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1071 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Llama 2-70B: 32.2 (#265)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Longer Query | 1422 | 1062 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Llama 2-70B: 32.5 (#274)

| Benchmark | DeepSeek-V3.1 | Llama 2-70B |
|---|---|---|
| LMArena Text | 1420 | 1115 |
| LMArena Creative Writing | 1401 | 1075 |
| LMArena Multi-Turn | 1408 | 1088 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Llama 2-70B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 24.1 on the Noometry Index.

### Is DeepSeek-V3.1 or Llama 2-70B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 30.8 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Llama 2-70B share?

20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 2-70B has 35.