Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Opus 4.6
84
Winner · 4/8 categoriesQwen3.5-27B
71
2/8 categoriesClaude Opus 4.6· Qwen3.5-27B
Pick Claude Opus 4.6 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Claude Opus 4.6 is clearly ahead on the aggregate, 84 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Opus 4.6's sharpest advantage is in reasoning, where it averages 82.4 against 60.6. The single biggest benchmark swing on the page is LongBench v2, 92% to 60.6%. Qwen3.5-27B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Claude Opus 4.6 is also the more expensive model on tokens at $15.00 input / $75.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B is the reasoning model in the pair, while Claude Opus 4.6 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. Claude Opus 4.6 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 | Claude Opus 4.6 | Qwen3.5-27B |
|---|---|---|
| AgenticClaude Opus 4.6 wins | ||
| Terminal-Bench 2.0 | 65.4% | 41.6% |
| BrowseComp | 84% | 61% |
| OSWorld-Verified | — | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 80.8% | 72.4% |
| LiveCodeBench | 76% | 80.7% |
| FLTEval | 39.6% | — |
| SWE-Rebench | 65.3% | — |
| React Native Evals | 84.4% | — |
| Multimodal & GroundedClaude Opus 4.6 wins | ||
| MMMU-Pro | 77.3% | 75% |
| OfficeQA Pro | 94% | — |
| ReasoningClaude Opus 4.6 wins | ||
| MuSR | 93% | — |
| BBH | 94% | — |
| LongBench v2 | 92% | 60.6% |
| MRCRv2 | 76% | — |
| ARC-AGI-2 | 68.8% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 99% | — |
| GPQA | 91.3% | 85.5% |
| SuperGPQA | 95% | 65.6% |
| MMLU-Pro | 82% | 86.1% |
| HLE | 53% | — |
| FrontierScience | 88% | — |
| SimpleQA | 72% | — |
| Instruction Following | ||
| IFEval | — | 95% |
| MultilingualClaude Opus 4.6 wins | ||
| MGSM | 96% | — |
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 98% | — |
Claude Opus 4.6 is ahead overall, 84 to 71. The biggest single separator in this matchup is LongBench v2, where the scores are 92% and 60.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 77.8. 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 72. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for reasoning in this comparison, averaging 82.4 versus 60.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for agentic tasks in this comparison, averaging 72.6 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for multimodal and grounded tasks in this comparison, averaging 84.8 versus 75. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for multilingual tasks in this comparison, averaging 96 versus 82.2. Qwen3.5-27B stays close enough that the answer can still flip depending on your workload.
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