Model comparison
Llama 3.1-8B vs Qwen3 Max
Qwen3 Max is the stronger model overall, scoring 43.7 to 23.0 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.
Last verified . 24 shared benchmarks.
Meta
Alibaba (Qwen)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.
Side by side
| Llama 3.1-8B | Qwen3 Max | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 23.0 | 43.7 |
| Released | 2024-07-23 | 2025-09-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.05 | $1.20 |
| Output $ / M tokens | $0.08 | $6 |
| Results tracked | 43 | 33 |
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Category by category
Coding Qwen3 Max leads
Llama 3.1-8B: 20.2 (#340), Qwen3 Max: 43.0 (#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 Not comparable
Llama 3.1-8B: 22.5 (#131), Qwen3 Max: —
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
| Vending-Bench 2 | — | 71.56 |
Reasoning Qwen3 Max leads
Llama 3.1-8B: 14.9 (#321), Qwen3 Max: 22.6 (#190)
| 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 | 116.57 | 142.38 |
| Kagi LLM Benchmark | — | 72.5% |
| NYT Connections (extended) | — | 30.1% |
| CritPt | 0% | — |
| Mystery Game Puzzles | — | 5% |
| PIQA | 81.2% | — |
Math Qwen3 Max leads
Llama 3.1-8B: 10.2 (#317), Qwen3 Max: 38.7 (#131)
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 73.3% |
| LMArena Math | 1179 | 1446 |
| MATH Level 5 | 22.9% | 97.1% |
| FrontierMath (Tiers 1-3) | — | 18.9% |
| Omni-MATH | 13.7% | — |
| GSM8K | 82.4% | — |
Knowledge Qwen3 Max leads
Llama 3.1-8B: 8.0 (#307), Qwen3 Max: 48.1 (#78)
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 27% | 72.6% |
| LMArena Expert | 1144 | 1455 |
| SimpleQA Verified | — | 48.7% |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Qwen3 Max leads
Llama 3.1-8B: 34.0 (#249), Qwen3 Max: 53.7 (#62)
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1148 | 1429 |
| LMArena Chinese | 1151 | 1478 |
| LMArena French | 1177 | 1449 |
| LMArena German | 1144 | 1463 |
| LMArena Japanese | 1061 | 1397 |
| LMArena Korean | 1053 | 1399 |
| LMArena Russian | 1158 | 1428 |
| LMArena Spanish | 1169 | 1462 |
Instruction Following Qwen3 Max leads
Llama 3.1-8B: 58.9 (#258), Qwen3 Max: 74.8 (#87)
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1159 | 1419 |
| IFEval | 74.3% | — |
Long Context Qwen3 Max leads
Llama 3.1-8B: 35.8 (#238), Qwen3 Max: 41.6 (#134)
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Longer Query | 1182 | 1438 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 14.5% |
Writing & Preference Qwen3 Max leads
Llama 3.1-8B: 29.7 (#290), Qwen3 Max: 62.4 (#76)
| Benchmark | Llama 3.1-8B | Qwen3 Max |
|---|---|---|
| LMArena Text | 1187 | 1439 |
| LMArena Creative Writing | 1154 | 1402 |
| LMArena Multi-Turn | 1172 | 1446 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Qwen3 Max?
Qwen3 Max is the stronger model overall, scoring 43.7 to 23.0 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: 43.0 versus 20.2 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.