Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen2.5-72B
61
2/8 categoriesQwen3.5-27B
71
Winner · 5/8 categoriesQwen2.5-72B· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Qwen2.5-72B 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.
Qwen3.5-27B is clearly ahead on the aggregate, 71 to 61. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in coding, where it averages 77.6 against 44.1. The single biggest benchmark swing on the page is LiveCodeBench, 40% to 80.7%. Qwen2.5-72B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-27B is the reasoning model in the pair, while Qwen2.5-72B 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 128K for Qwen2.5-72B.
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 | Qwen2.5-72B | Qwen3.5-27B |
|---|---|---|
| AgenticQwen2.5-72B wins | ||
| Terminal-Bench 2.0 | 56% | 41.6% |
| BrowseComp | 64% | 61% |
| OSWorld-Verified | 55% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 75% | — |
| SWE-bench Verified | 46% | 72.4% |
| LiveCodeBench | 40% | 80.7% |
| SWE-bench Pro | 47% | — |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 64% | 75% |
| OfficeQA Pro | 70% | — |
| ReasoningQwen2.5-72B wins | ||
| MuSR | 78% | — |
| BBH | 81% | — |
| MRCRv2 | 71% | — |
| LongBench v2 | — | 60.6% |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 83% | — |
| GPQA | 82% | 85.5% |
| SuperGPQA | 80% | 65.6% |
| MMLU-Pro | 75% | 86.1% |
| HLE | 11% | — |
| FrontierScience | 70% | — |
| SimpleQA | 80% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 85% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MGSM | 84% | — |
| MMLU-ProX | 79% | 82.2% |
| Mathematics | ||
| AIME 2023 | 84% | — |
| AIME 2024 | 86% | — |
| AIME 2025 | 85% | — |
| HMMT Feb 2023 | 80% | — |
| HMMT Feb 2024 | 82% | — |
| HMMT Feb 2025 | 81% | — |
| BRUMO 2025 | 83% | — |
| MATH-500 | 84% | — |
Qwen3.5-27B is ahead overall, 71 to 61. The biggest single separator in this matchup is LiveCodeBench, where the scores are 40% and 80.7%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 61.5. 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 44.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen2.5-72B has the edge for reasoning in this comparison, averaging 74.1 versus 60.6. Qwen3.5-27B stays close enough that the answer can still flip depending on your workload.
Qwen2.5-72B has the edge for agentic tasks in this comparison, averaging 57.7 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multimodal and grounded tasks in this comparison, averaging 75 versus 66.7. 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 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 80.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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