# DeepSeek-V3.1 vs Llama 3-8B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 25.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-3-8b
- 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 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 7.8.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 43.9% for Llama 3-8B.

## Snapshot

| | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 25.5 |
| Rank | 108 | 344 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Llama 3-8B: 31.0 (#289)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1417 | 1152 |
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Llama 3-8B: 14.3 (#326)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1133 |
| DTBench | 82.7% | 43.9% |
| Epoch Capabilities Index | 139.92 | 116.45 |
| ForecastBench | 58 | 58.6 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 0% |
| LMCA | 24.3% | — |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Llama 3-8B: 8.8 (#323)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Math | 1420 | 1151 |
| OTIS Mock AIME 2024-2025 | — | 1.9% |
| MATH Level 5 | — | 6.1% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Llama 3-8B: 7.8 (#308)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Expert | 1405 | 1113 |
| GPQA Diamond | — | 26.1% |
| Vectara Hallucination Rate | 5.5% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Llama 3-8B: 30.8 (#261)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1400 | 1098 |
| LMArena Chinese | 1469 | 1076 |
| LMArena French | 1447 | 1159 |
| LMArena German | 1411 | 1104 |
| LMArena Japanese | 1378 | 967 |
| LMArena Korean | 1337 | 1004 |
| LMArena Russian | 1405 | 1109 |
| LMArena Spanish | 1431 | 1173 |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Llama 3-8B: 58.4 (#260)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1127 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Llama 3-8B: 34.2 (#251)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1422 | 1128 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Llama 3-8B: 37.5 (#256)

| Benchmark | DeepSeek-V3.1 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1420 | 1166 |
| LMArena Creative Writing | 1401 | 1150 |
| LMArena Multi-Turn | 1408 | 1152 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Llama 3-8B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 25.5 on the Noometry Index.

### Is DeepSeek-V3.1 or Llama 3-8B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 31.0 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Llama 3-8B share?

20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 3-8B has 34.