Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Sonnet 4.6
80
Winner · 4/8 categoriesQwen3.5-35B-A3B
67
3/8 categoriesClaude Sonnet 4.6· Qwen3.5-35B-A3B
Pick Claude Sonnet 4.6 if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Claude Sonnet 4.6 is clearly ahead on the aggregate, 80 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Sonnet 4.6's sharpest advantage is in reasoning, where it averages 78 against 59. The single biggest benchmark swing on the page is SuperGPQA, 95% to 63.4%. Qwen3.5-35B-A3B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.6 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. Qwen3.5-35B-A3B is the reasoning model in the pair, while Claude Sonnet 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 200K for Claude Sonnet 4.6.
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 Sonnet 4.6 | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticClaude Sonnet 4.6 wins | ||
| Terminal-Bench 2.0 | 59.1% | 40.5% |
| BrowseComp | 77% | 61% |
| OSWorld-Verified | 72.5% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 93% | — |
| SWE-bench Verified | 79.6% | 69.2% |
| LiveCodeBench | 54% | 74.6% |
| SWE-bench Pro | 64% | — |
| FLTEval | 23.7% | — |
| SWE-Rebench | 60.7% | — |
| React Native Evals | 77.9% | — |
| Multimodal & GroundedClaude Sonnet 4.6 wins | ||
| OfficeQA Pro | 88% | — |
| MMMU-Pro | — | 75.1% |
| ReasoningClaude Sonnet 4.6 wins | ||
| MuSR | 93% | — |
| BBH | 88% | — |
| LongBench v2 | 83% | 59% |
| MRCRv2 | 79% | — |
| ARC-AGI-2 | 59% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| GPQA | 89.9% | 84.2% |
| SuperGPQA | 95% | 63.4% |
| MMLU-Pro | 79.2% | 85.3% |
| HLE | 49% | — |
| FrontierScience | 85% | — |
| SimpleQA | 48.5% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 89.5% | 91.9% |
| MultilingualClaude Sonnet 4.6 wins | ||
| MGSM | 91% | — |
| MMLU-ProX | 89% | 81% |
| Mathematics | ||
| MATH-500 | 97.8% | — |
Claude Sonnet 4.6 is ahead overall, 80 to 67. The biggest single separator in this matchup is SuperGPQA, where the scores are 95% and 63.4%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 72.5. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for coding in this comparison, averaging 72.6 versus 62.7. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for reasoning in this comparison, averaging 78 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for agentic tasks in this comparison, averaging 68.3 versus 50.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for multimodal and grounded tasks in this comparison, averaging 88 versus 75.1. Qwen3.5-35B-A3B stays close enough that the answer can still flip depending on your workload.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 89.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for multilingual tasks in this comparison, averaging 89.7 versus 81. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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