Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek R1 Distill Qwen 32B
~3
0/8 categoriesQwen3.6 Plus
69
Winner · 0/8 categoriesDeepSeek R1 Distill Qwen 32B· Qwen3.6 Plus
Benchmark data for DeepSeek R1 Distill Qwen 32B and Qwen3.6 Plus is coming soon on BenchLM.
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Qwen3.6 Plus has the larger context window at 1M, compared with 128K for DeepSeek R1 Distill Qwen 32B.
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 R1 Distill Qwen 32B | Qwen3.6 Plus |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 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% |
| Coding | ||
| React Native Evals | 31.8% | — |
| SWE-bench Verified | — | 78.8% |
| SWE-bench Pro | — | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & Grounded | ||
| 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% |
| Reasoning | ||
| AI-Needle | — | 68.3% |
| LongBench v2 | — | 62% |
| Knowledge | ||
| GPQA | — | 90.4% |
| SuperGPQA | — | 71.6% |
| MMLU-Pro | — | 88.5% |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| HLE | — | 28.8% |
| Instruction Following | ||
| IFEval | — | 94.3% |
| IFBench | — | 74.2% |
| Multilingual | ||
| MMLU-ProX | — | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
DeepSeek R1 Distill Qwen 32B: $0.00 input / $0.00 output per 1M tokens Qwen3.6 Plus: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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