Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen3.5-27B
70
Winner · 7/8 categoriesZ-1
47
0/8 categoriesQwen3.5-27B· Z-1
Pick Qwen3.5-27B if you want the stronger benchmark profile. Z-1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 47. 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 27.6. The single biggest benchmark swing on the page is LiveCodeBench, 80.7% to 22%.
Qwen3.5-27B is the reasoning model in the pair, while Z-1 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 Z-1.
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 | Z-1 |
|---|---|---|
| AgenticQwen3.5-27B wins | ||
| Terminal-Bench 2.0 | 41.6% | 39% |
| BrowseComp | 61% | 49% |
| OSWorld-Verified | 56.2% | 41% |
| tau2-bench | 79% | — |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 72.4% | 33% |
| LiveCodeBench | 80.7% | 22% |
| HumanEval | — | 44% |
| SWE-bench Pro | — | 30% |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 75% | 46% |
| OfficeQA Pro | — | 56% |
| ReasoningQwen3.5-27B wins | ||
| LongBench v2 | 60.6% | 56% |
| MuSR | — | 48% |
| BBH | — | 74% |
| MRCRv2 | — | 57% |
| KnowledgeQwen3.5-27B wins | ||
| MMLU-Pro | 86.1% | 64% |
| SuperGPQA | 65.6% | 49% |
| GPQA | 85.5% | 51% |
| MMLU | — | 52% |
| HLE | — | 6% |
| FrontierScience | — | 51% |
| SimpleQA | — | 50% |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 95% | 80% |
| MultilingualQwen3.5-27B wins | ||
| MMLU-ProX | 82.2% | 72% |
| MGSM | — | 74% |
| Mathematics | ||
| AIME 2023 | — | 52% |
| AIME 2024 | — | 54% |
| AIME 2025 | — | 53% |
| HMMT Feb 2023 | — | 48% |
| HMMT Feb 2024 | — | 50% |
| HMMT Feb 2025 | — | 49% |
| BRUMO 2025 | — | 51% |
| MATH-500 | — | 73% |
Qwen3.5-27B is ahead overall, 70 to 47. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80.7% and 22%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 43.1. Inside this category, GPQA 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 27.6. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 54.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 51.6 versus 42.2. Inside this category, OSWorld-Verified 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 50.5. 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 80. 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 72.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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