# Llama 2-13B vs Qwen3-Next 80B-A3B Instruct

> Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 29.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/llama-2-13b-vs-qwen3-next-80b-a3b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Llama 2-13B scores higher in 0 categories and Qwen3-Next 80B-A3B Instruct in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3-Next 80B-A3B Instruct leads 58.0 to 29.8.

## Snapshot

| | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 29.6 | 43.0 |
| Rank | 309 | 102 |
| Context | — | 131K |
| Input $/M | — | $0.50 |
| Output $/M | — | $2 |
| Weights | Open | Open |

## Coding

- Llama 2-13B: 30.9 (#291)
- Qwen3-Next 80B-A3B Instruct: 42.5 (#98)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1062 | 1440 |

## Reasoning

- Llama 2-13B: 12.8 (#337)
- Qwen3-Next 80B-A3B Instruct: 31.1 (#81)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1051 | 1428 |
| Kagi LLM Benchmark | — | 66.7% |
| Chess Puzzles | 0% | — |
| DTBench | 42.2% | — |
| BIG-Bench Hard | 58.2% | — |
| Epoch Capabilities Index | 106.17 | — |
| HellaSwag | 80.7% | — |
| LAMBADA | 76.5% | — |
| PIQA | 80.8% | — |
| WinoGrande | 72.8% | — |

## Math

- Llama 2-13B: 31.1 (#229)
- Qwen3-Next 80B-A3B Instruct: 38.8 (#126)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1065 | 1440 |
| Omni-MATH | — | 46.7% |
| GSM8K | 36.9% | — |

## Knowledge

- Llama 2-13B: 28.1 (#249)
- Qwen3-Next 80B-A3B Instruct: 41.8 (#106)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Expert | 1030 | 1417 |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |
| ARC (AI2) Challenge | 60.3% | — |
| BoolQ | 82.4% | — |
| MMLU | 55.6% | — |
| OpenBookQA | 57% | — |
| TriviaQA | 79.6% | — |

## Multimodal

- Llama 2-13B: —
- Qwen3-Next 80B-A3B Instruct: —

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| ScienceQA | 55.8% | — |

## Multilingual

- Llama 2-13B: 26.5 (#279)
- Qwen3-Next 80B-A3B Instruct: 52.1 (#93)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1024 | 1407 |
| LMArena Chinese | 1001 | 1460 |
| LMArena French | 1044 | 1413 |
| LMArena German | 1009 | 1417 |
| LMArena Japanese | 894 | 1395 |
| LMArena Korean | 953 | 1364 |
| LMArena Russian | 1055 | 1404 |
| LMArena Spanish | 1087 | 1435 |

## Instruction Following

- Llama 2-13B: 53.3 (#287)
- Qwen3-Next 80B-A3B Instruct: 70.8 (#159)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1045 | 1389 |
| IFEval | — | 81% |

## Long Context

- Llama 2-13B: 32.3 (#269)
- Qwen3-Next 80B-A3B Instruct: 37.0 (#223)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1064 | 1403 |
| Fiction.LiveBench | — | 55.6% |

## Writing & Preference

- Llama 2-13B: 29.8 (#289)
- Qwen3-Next 80B-A3B Instruct: 58.0 (#121)

| Benchmark | Llama 2-13B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1084 | 1417 |
| LMArena Creative Writing | 1047 | 1334 |
| LMArena Multi-Turn | 1050 | 1416 |
| WildBench | — | 80.7% |

## FAQ

### Is Llama 2-13B better than Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 29.6 on the Noometry Index.

### Is Llama 2-13B or Qwen3-Next 80B-A3B Instruct better for coding?

Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 30.9 in the Noometry coding category.

### How many benchmarks do Llama 2-13B and Qwen3-Next 80B-A3B Instruct share?

17 benchmarks have published results for both models. Llama 2-13B has 32 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.