# Llama 3.1-8B vs Qwen3-Coder 480B-A35B Instruct

> Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 52× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-1-8b-vs-qwen3-coder-480b-a35b-instruct
- Last updated: 2026-10-12
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and Qwen3-Coder 480B-A35B Instruct in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3-Coder 480B-A35B Instruct leads 37.0 to 8.0.
- The biggest single-benchmark swing is WeirdML: 1.7% for Llama 3.1-8B and 41.2% for Qwen3-Coder 480B-A35B Instruct.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct accepts more context: 262K tokens versus 128K.

## Snapshot

| | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 23.0 | 38.1 |
| Rank | 352 | 190 |
| Context | 128K | 262K |
| Input $/M | $0.05 | $1.50 |
| Output $/M | $0.08 | $7.50 |
| Weights | Open | Open |

## Coding

- Llama 3.1-8B: 20.2 (#340)
- Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| WeirdML | 1.7% | 41.2% |
| LMArena Coding | 1195 | 1412 |
| SWE-bench Verified (bash only) | — | 55.4% |
| LMArena WebDev | — | 1275 |
| SciCode | 13.2% | — |
| GSO | — | 4.9% |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |

## Agentic & Tool Use

- Llama 3.1-8B: 22.5 (#131)
- Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |

## Reasoning

- Llama 3.1-8B: 14.9 (#321)
- Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.57 | — |
| PIQA | 81.2% | — |

## Math

- Llama 3.1-8B: 10.2 (#317)
- Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1179 | 1365 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |

## Knowledge

- Llama 3.1-8B: 8.0 (#307)
- Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1144 | 1338 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |

## Multilingual

- Llama 3.1-8B: 34.0 (#249)
- Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1148 | 1346 |
| LMArena Chinese | 1151 | 1357 |
| LMArena French | 1177 | 1398 |
| LMArena German | 1144 | 1325 |
| LMArena Japanese | 1061 | 1310 |
| LMArena Korean | 1053 | 1305 |
| LMArena Russian | 1158 | 1366 |
| LMArena Spanish | 1169 | 1360 |

## Instruction Following

- Llama 3.1-8B: 58.9 (#258)
- Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1159 | 1355 |
| IFEval | 74.3% | — |

## Long Context

- Llama 3.1-8B: 35.8 (#238)
- Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1182 | 1378 |

## Writing & Preference

- Llama 3.1-8B: 29.7 (#290)
- Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)

| Benchmark | Llama 3.1-8B | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1187 | 1357 |
| LMArena Creative Writing | 1154 | 1333 |
| LMArena Multi-Turn | 1172 | 1365 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |

## FAQ

### Is Llama 3.1-8B better than Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is the stronger model overall, scoring 38.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 52× less per token, which makes it the better buy when Qwen3-Coder 480B-A35B Instruct's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.1-8B or Qwen3-Coder 480B-A35B Instruct?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.

### Is Llama 3.1-8B or Qwen3-Coder 480B-A35B Instruct better for coding?

Qwen3-Coder 480B-A35B Instruct scores higher on coding benchmarks: 35.5 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Qwen3-Coder 480B-A35B Instruct does, with 262K tokens against 128K.

### How many benchmarks do Llama 3.1-8B and Qwen3-Coder 480B-A35B Instruct share?

18 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.