# DeepSeek-V3.1 vs Llama 13b

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 24.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-13b
- Last updated: 2026-10-10
- Shared benchmarks: 9

## Summary

- They share 9 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 6 categories and Llama 13b in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 13.8.

## Snapshot

| | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 24.4 |
| Rank | 108 | 348 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Llama 13b: 21.4 (#337)

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| LMArena Coding | 1417 | 683 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Llama 13b: 14.0 (#329)

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| LMArena Hard Prompts | 1417 | 728 |
| Epoch Capabilities Index | 139.92 | 100.58 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| BIG-Bench Hard | — | 37.9% |
| ForecastBench | 58 | — |
| HellaSwag | — | 79.2% |
| LAMBADA | — | 75.2% |
| PIQA | — | 80.1% |
| WinoGrande | — | 73% |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Llama 13b: 26.7 (#256)

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| LMArena Math | 1420 | 838 |
| GSM8K | — | 20.6% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Llama 13b: —

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |
| ARC (AI2) Challenge | — | 52.7% |
| BoolQ | — | 78.7% |
| MMLU | — | 47.7% |
| OpenBookQA | — | 56.4% |
| TriviaQA | — | 77.9% |

## Multimodal

- DeepSeek-V3.1: —
- Llama 13b: —

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| ScienceQA | — | 43.3% |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Llama 13b: 16.6 (#297)

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| LMArena Non-English | 1400 | 819 |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Llama 13b: 36.7 (#305)

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| LMArena Instruction Following | 1400 | 781 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Llama 13b: —

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

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Llama 13b: 13.8 (#312)

| Benchmark | DeepSeek-V3.1 | Llama 13b |
|---|---|---|
| LMArena Text | 1420 | 834 |
| LMArena Creative Writing | 1401 | 794 |
| LMArena Multi-Turn | 1408 | 753 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

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

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 24.4 on the Noometry Index.

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

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 21.4 in the Noometry coding category.

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

9 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 13b has 21.