Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Llama 3.1 405B
53
1/8 categoriesQwen3.6 Plus
69
Winner · 6/8 categoriesLlama 3.1 405B· Qwen3.6 Plus
Pick Qwen3.6 Plus if you want the stronger benchmark profile. Llama 3.1 405B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6 Plus is clearly ahead on the aggregate, 69 to 53. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6 Plus's sharpest advantage is in coding, where it averages 64.9 against 41.4. The single biggest benchmark swing on the page is SWE-bench Verified, 46% to 78.8%. Llama 3.1 405B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Qwen3.6 Plus is the reasoning model in the pair, while Llama 3.1 405B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.6 Plus gives you the larger context window at 1M, compared with 128K for Llama 3.1 405B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Llama 3.1 405B | Qwen3.6 Plus |
|---|---|---|
| AgenticQwen3.6 Plus wins | ||
| Terminal-Bench 2.0 | 53% | 61.6% |
| Claw-Eval | — | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| DeepPlanning | — | 41.5% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| OSWorld-Verified | — | 62.5% |
| CodingQwen3.6 Plus wins | ||
| HumanEval | 62% | — |
| SWE-bench Verified | 46% | 78.8% |
| LiveCodeBench | 37% | — |
| SWE-bench Pro | 43% | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| OfficeQA Pro | 65% | — |
| MMMU | — | 86.0% |
| MMMU-Pro | — | 78.8% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| ReasoningLlama 3.1 405B wins | ||
| BBH | 82% | — |
| LongBench v2 | 68% | 62% |
| MRCRv2 | 65% | — |
| AI-Needle | — | 68.3% |
| KnowledgeQwen3.6 Plus wins | ||
| MMLU | 70% | — |
| GPQA | 70% | 90.4% |
| SuperGPQA | 68% | 71.6% |
| MMLU-Pro | 71% | 88.5% |
| HLE | 7% | 28.8% |
| FrontierScience | 65% | — |
| SimpleQA | 68% | — |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| Instruction FollowingQwen3.6 Plus wins | ||
| IFEval | 86% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualQwen3.6 Plus wins | ||
| MMLU-ProX | 78% | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME 2023 | 70% | — |
| AIME 2024 | 72% | — |
| HMMT Feb 2024 | 68% | — |
| MATH-500 | 82% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
Qwen3.6 Plus is ahead overall, 69 to 53. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 46% and 78.8%.
Qwen3.6 Plus has the edge for knowledge tasks in this comparison, averaging 66 versus 54.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for coding in this comparison, averaging 64.9 versus 41.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Llama 3.1 405B has the edge for reasoning in this comparison, averaging 66.6 versus 62. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 62 versus 53. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multimodal and grounded tasks in this comparison, averaging 78.8 versus 65. Llama 3.1 405B stays close enough that the answer can still flip depending on your workload.
Qwen3.6 Plus has the edge for instruction following in this comparison, averaging 94.3 versus 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multilingual tasks in this comparison, averaging 84.7 versus 78. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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