# Llama 3.1-8B vs Qwen3 Max

> Qwen3 Max is the stronger model overall, scoring 43.5 to 22.8 on the Noometry Index. Llama 3.1-8B costs 42× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-1-8b-vs-qwen3-max
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and Qwen3 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 Max leads 48.1 to 8.0.
- The biggest single-benchmark swing is MATH Level 5: 22.9% for Llama 3.1-8B and 97.1% for Qwen3 Max.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $1.20 / $6 for Qwen3 Max.
- Qwen3 Max accepts more context: 262K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 22.8 | 43.5 |
| Rank | 347 | 82 |
| Context | 128K | 262K |
| Input $/M | $0.05 | $1.20 |
| Output $/M | $0.08 | $6 |
| Weights | Open | Proprietary |

## Coding

- Llama 3.1-8B: 19.9 (#335)
- Qwen3 Max: 42.4 (#93)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Coding | 1195 | 1456 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 370.45 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |

## Agentic & Tool Use

- Llama 3.1-8B: 22.3 (#123)
- Qwen3 Max: —

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
| Vending-Bench 2 | — | 71.56 |

## Reasoning

- Llama 3.1-8B: 14.5 (#316)
- Qwen3 Max: 22.0 (#186)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| Chess Puzzles | 0% | 4% |
| LMArena Hard Prompts | 1175 | 1448 |
| DTBench | 50.9% | 82.1% |
| LMCA | 5.4% | 28.3% |
| Epoch Capabilities Index | 116.48 | 142.41 |
| Kagi LLM Benchmark | — | 72.5% |
| NYT Connections (extended) | — | 30.1% |
| CritPt | 0% | — |
| Mystery Game Puzzles | — | 5% |
| PIQA | 81.2% | — |

## Math

- Llama 3.1-8B: 9.5 (#311)
- Qwen3 Max: 37.6 (#94)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 73.3% |
| LMArena Math | 1179 | 1447 |
| MATH Level 5 | 22.9% | 97.1% |
| FrontierMath (Tiers 1-3) | — | 18.9% |
| Omni-MATH | 13.7% | — |
| GSM8K | 82.4% | — |

## Knowledge

- Llama 3.1-8B: 8.0 (#302)
- Qwen3 Max: 48.1 (#72)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 27% | 72.6% |
| LMArena Expert | 1144 | 1457 |
| SimpleQA Verified | — | 48.7% |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |

## Multilingual

- Llama 3.1-8B: 34.2 (#244)
- Qwen3 Max: 53.9 (#58)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1148 | 1429 |
| LMArena Chinese | 1151 | 1479 |
| LMArena French | 1176 | 1450 |
| LMArena German | 1144 | 1463 |
| LMArena Japanese | 1061 | 1399 |
| LMArena Korean | 1053 | 1397 |
| LMArena Russian | 1158 | 1428 |
| LMArena Spanish | 1169 | 1463 |

## Instruction Following

- Llama 3.1-8B: 59.1 (#253)
- Qwen3 Max: 75.0 (#81)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1159 | 1420 |
| IFEval | 74.3% | — |

## Long Context

- Llama 3.1-8B: 35.8 (#233)
- Qwen3 Max: 41.6 (#129)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Longer Query | 1182 | 1439 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 14.5% |

## Writing & Preference

- Llama 3.1-8B: 29.9 (#285)
- Qwen3 Max: 62.6 (#72)

| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Text | 1187 | 1439 |
| LMArena Creative Writing | 1154 | 1401 |
| LMArena Multi-Turn | 1172 | 1446 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |

## FAQ

### Is Llama 3.1-8B better than Qwen3 Max?

Qwen3 Max is the stronger model overall, scoring 43.5 to 22.8 on the Noometry Index. Llama 3.1-8B costs 42× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.

### Which is cheaper, Llama 3.1-8B or Qwen3 Max?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Qwen3 Max lists at $1.20 and $6.

### Is Llama 3.1-8B or Qwen3 Max better for coding?

Qwen3 Max scores higher on coding benchmarks: 42.4 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Qwen3 Max does, with 262K tokens against 128K.

### How many benchmarks do Llama 3.1-8B and Qwen3 Max share?

24 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Qwen3 Max has 33.