# DeepSeek-V3.1 vs Llama 2-13B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 29.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-2-13b
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama 2-13B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 30.0.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 42.2% for Llama 2-13B.

## Snapshot

| | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 29.1 |
| Rank | 106 | 304 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Llama 2-13B: 30.4 (#287)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Coding | 1417 | 1062 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Llama 2-13B: 12.3 (#333)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1051 |
| DTBench | 82.7% | 42.2% |
| Epoch Capabilities Index | 139.92 | 105.8 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 0% |
| LMCA | 24.3% | — |
| BIG-Bench Hard | — | 58.2% |
| ForecastBench | 58 | — |
| HellaSwag | — | 80.7% |
| LAMBADA | — | 76.5% |
| PIQA | — | 80.8% |
| WinoGrande | — | 72.8% |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Llama 2-13B: 28.7 (#226)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Math | 1420 | 1065 |
| GSM8K | — | 36.9% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Llama 2-13B: 27.5 (#244)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Expert | 1403 | 1030 |
| Vectara Hallucination Rate | 5.5% | — |
| ARC (AI2) Challenge | — | 60.3% |
| BoolQ | — | 82.4% |
| MMLU | — | 55.6% |
| OpenBookQA | — | 57% |
| TriviaQA | — | 79.6% |

## Multimodal

- DeepSeek-V3.1: —
- Llama 2-13B: —

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| ScienceQA | — | 55.8% |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Llama 2-13B: 26.7 (#274)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Non-English | 1400 | 1024 |
| LMArena Chinese | 1470 | 1001 |
| LMArena French | 1447 | 1043 |
| LMArena German | 1412 | 1009 |
| LMArena Japanese | 1374 | 894 |
| LMArena Korean | 1337 | 953 |
| LMArena Russian | 1405 | 1055 |
| LMArena Spanish | 1429 | 1086 |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Llama 2-13B: 53.5 (#282)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1046 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Llama 2-13B: 32.3 (#264)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Longer Query | 1422 | 1064 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Llama 2-13B: 30.0 (#284)

| Benchmark | DeepSeek-V3.1 | Llama 2-13B |
|---|---|---|
| LMArena Text | 1420 | 1085 |
| LMArena Creative Writing | 1401 | 1047 |
| LMArena Multi-Turn | 1408 | 1050 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Llama 2-13B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 29.1 on the Noometry Index.

### Is DeepSeek-V3.1 or Llama 2-13B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 30.4 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Llama 2-13B share?

19 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 2-13B has 32.