Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0
62
2/8 categoriesQwen3.6 Plus
69
Winner · 5/8 categoriesDeepSeek Coder 2.0· Qwen3.6 Plus
Pick Qwen3.6 Plus if you want the stronger benchmark profile. DeepSeek Coder 2.0 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 62. 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 multimodal & grounded, where it averages 78.8 against 58.6. The single biggest benchmark swing on the page is MMMU-Pro, 50% to 78.8%. DeepSeek Coder 2.0 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6 Plus. That is roughly Infinityx on output cost alone. Qwen3.6 Plus is the reasoning model in the pair, while DeepSeek Coder 2.0 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 DeepSeek Coder 2.0.
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 | DeepSeek Coder 2.0 | Qwen3.6 Plus |
|---|---|---|
| AgenticDeepSeek Coder 2.0 wins | ||
| Terminal-Bench 2.0 | 73% | 61.6% |
| BrowseComp | 62% | — |
| OSWorld-Verified | 65% | 62.5% |
| 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% |
| CodingQwen3.6 Plus wins | ||
| HumanEval | 82% | — |
| SWE-bench Verified | 51% | 78.8% |
| LiveCodeBench | 45% | — |
| SWE-bench Pro | 61% | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| MMMU-Pro | 50% | 78.8% |
| OfficeQA Pro | 69% | — |
| MMMU | — | 86.0% |
| 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% |
| ReasoningDeepSeek Coder 2.0 wins | ||
| MuSR | 76% | — |
| BBH | 84% | — |
| LongBench v2 | 73% | 62% |
| MRCRv2 | 71% | — |
| AI-Needle | — | 68.3% |
| KnowledgeQwen3.6 Plus wins | ||
| MMLU | 80% | — |
| GPQA | 79% | 90.4% |
| SuperGPQA | 77% | 71.6% |
| MMLU-Pro | 73% | 88.5% |
| HLE | 14% | 28.8% |
| FrontierScience | 72% | — |
| SimpleQA | 78% | — |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| Instruction FollowingQwen3.6 Plus wins | ||
| IFEval | 86% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualQwen3.6 Plus wins | ||
| MGSM | 83% | — |
| 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 | 81% | — |
| AIME 2024 | 83% | — |
| AIME 2025 | 82% | — |
| HMMT Feb 2023 | 77% | — |
| HMMT Feb 2024 | 79% | — |
| HMMT Feb 2025 | 78% | — |
| BRUMO 2025 | 80% | — |
| MATH-500 | 81% | — |
| 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 62. The biggest single separator in this matchup is MMMU-Pro, where the scores are 50% and 78.8%.
Qwen3.6 Plus has the edge for knowledge tasks in this comparison, averaging 66 versus 61.1. Inside this category, MMLU-Pro 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 52.5. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for reasoning in this comparison, averaging 73.1 versus 62. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for agentic tasks in this comparison, averaging 67.5 versus 62. 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 58.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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 79.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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