Model comparison
Llama 3.1-70B vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 29.6 on the Noometry Index. Llama 3.1-70B costs 7.5× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. Llama 3.1-70B scores higher in 0 categories and Qwen3.8 Max in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 3.6% for Llama 3.1-70B and 100% for Qwen3.8 Max.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-70B | Qwen3.8 Max | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 29.6 | 56.8 |
| Released | 2024-07-23 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.40 | $2 |
| Output $ / M tokens | $0.40 | $6 |
| Results tracked | 35 | 39 |
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Category by category
Coding Qwen3.8 Max leads
Llama 3.1-70B: 30.3 (#296), Qwen3.8 Max: 53.5 (#29)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Coding | 1260 | 1502 |
| DeepSWE | — | 57.5% |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| SciCode | — | 53.2% |
| WeirdML | 9% | — |
| BigCodeBench Instruct | 46.1% | — |
| BigCodeBench Complete | 54.8% | — |
Agentic & Tool Use Qwen3.8 Max leads
Llama 3.1-70B: 25.1 (#112), Qwen3.8 Max: 45.4 (#14)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| TheAgentCompany | 6.9% | — |
| τ²-bench Banking | — | 55.1% |
| BALROG | 27.9% | — |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
Llama 3.1-70B: 21.6 (#220), Qwen3.8 Max: 54.4 (#26)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1496 |
| DTBench | 60% | 92% |
| LMCA | 14.8% | 46.2% |
| Epoch Capabilities Index | 125.92 | 156.41 |
| NYT Connections (extended) | — | 88.3% |
| CritPt | — | 20% |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 38% |
Math Qwen3.8 Max leads
Llama 3.1-70B: 13.5 (#304), Qwen3.8 Max: 73.2 (#20)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | 100% |
| LMArena Math | 1252 | 1499 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 21% | — |
| MATH Level 5 | 36.7% | — |
Knowledge Qwen3.8 Max leads
Llama 3.1-70B: 24.2 (#269), Qwen3.8 Max: 61.7 (#27)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 44.2% | 92.7% |
| LMArena Expert | 1209 | 1507 |
| SimpleQA Verified | — | 47.3% |
| MMLU-Pro | 65.3% | — |
| GPQA (HELM) | 42.6% | — |
| MMLU | 80.1% | — |
Multimodal Not comparable
Llama 3.1-70B: —, Qwen3.8 Max: 37.2 (#75)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
Llama 3.1-70B: 38.8 (#225), Qwen3.8 Max: 56.7 (#18)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1219 | 1472 |
| LMArena Chinese | 1215 | 1538 |
| LMArena French | 1261 | 1503 |
| LMArena German | 1222 | 1483 |
| LMArena Japanese | 1132 | 1467 |
| LMArena Korean | 1140 | 1461 |
| LMArena Russian | 1234 | 1481 |
| LMArena Spanish | 1253 | 1492 |
Instruction Following Qwen3.8 Max leads
Llama 3.1-70B: 65.3 (#223), Qwen3.8 Max: 77.6 (#17)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1231 | 1479 |
| IFEval | 82.1% | — |
Long Context Qwen3.8 Max leads
Llama 3.1-70B: 37.6 (#214), Qwen3.8 Max: 45.6 (#31)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1241 | 1489 |
Writing & Preference Qwen3.8 Max leads
Llama 3.1-70B: 35.4 (#267), Qwen3.8 Max: 67.1 (#30)
| Benchmark | Llama 3.1-70B | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1261 | 1483 |
| LMArena Creative Writing | 1232 | 1479 |
| LMArena Multi-Turn | 1256 | 1489 |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
Frequently asked questions
Is Llama 3.1-70B better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 29.6 on the Noometry Index. Llama 3.1-70B costs 7.5× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-70B or Qwen3.8 Max?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is Llama 3.1-70B or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 128K.
How many benchmarks do Llama 3.1-70B and Qwen3.8 Max share?
22 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Qwen3.8 Max has 39.