Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen3.5-27B
70
Winner · 3/8 categoriesQwen3.6 Plus
69
4/8 categoriesQwen3.5-27B· Qwen3.6 Plus
Pick Qwen3.5-27B if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Qwen3.5-27B finishes one point ahead overall, 70 to 69. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 66. The single biggest benchmark swing on the page is Claw-Eval, 20.2% to 58.7%. Qwen3.6 Plus does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.6 Plus gives you the larger context window at 1M, compared with 262K for Qwen3.5-27B.
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 | Qwen3.5-27B | Qwen3.6 Plus |
|---|---|---|
| AgenticQwen3.6 Plus wins | ||
| Terminal-Bench 2.0 | 41.6% | 61.6% |
| BrowseComp | 61% | — |
| OSWorld-Verified | 56.2% | 62.5% |
| Tau2-Telecom | 79% | — |
| Claw-Eval | 20.2% | 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% |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 72.4% | 78.8% |
| LiveCodeBench | 80.7% | — |
| SWE-bench Pro | — | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| MMMU-Pro | 75% | 78.8% |
| 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% |
| ReasoningQwen3.6 Plus wins | ||
| LongBench v2 | 60.6% | 62% |
| AI-Needle | — | 68.3% |
| KnowledgeQwen3.5-27B wins | ||
| MMLU-Pro | 86.1% | 88.5% |
| SuperGPQA | 65.6% | 71.6% |
| GPQA | 85.5% | 90.4% |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| HLE | — | 28.8% |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 95% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualQwen3.6 Plus wins | ||
| MMLU-ProX | 82.2% | 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% |
Qwen3.5-27B is ahead overall, 70 to 69. The biggest single separator in this matchup is Claw-Eval, where the scores are 20.2% and 58.7%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 66. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 64.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for reasoning in this comparison, averaging 62 versus 60.6. 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 51.6. Inside this category, Claw-Eval 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 75. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 94.3. 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 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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