Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1-preview
72
Winner · 4/8 categoriesQwen3.5-35B-A3B
67
3/8 categorieso1-preview· Qwen3.5-35B-A3B
Pick o1-preview if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if coding is the priority or you need the larger 262K context window.
o1-preview is clearly ahead on the aggregate, 72 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-preview's sharpest advantage is in reasoning, where it averages 85.4 against 59. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 77% to 40.5%. 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.
Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 200K for o1-preview.
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 | o1-preview | Qwen3.5-35B-A3B |
|---|---|---|
| Agentico1-preview wins | ||
| Terminal-Bench 2.0 | 77% | 40.5% |
| BrowseComp | 79% | 61% |
| OSWorld-Verified | 71% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 86% | — |
| SWE-bench Verified | 65% | 69.2% |
| LiveCodeBench | 60% | 74.6% |
| SWE-bench Pro | 69% | — |
| Multimodal & Groundedo1-preview wins | ||
| MMMU-Pro | 72% | 75.1% |
| OfficeQA Pro | 80% | — |
| Reasoningo1-preview wins | ||
| MuSR | 86% | — |
| BBH | 93% | — |
| LongBench v2 | 87% | 59% |
| MRCRv2 | 83% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 92% | — |
| GPQA | 90% | 84.2% |
| SuperGPQA | 88% | 63.4% |
| MMLU-Pro | 80% | 85.3% |
| HLE | 32% | — |
| FrontierScience | 83% | — |
| SimpleQA | 88% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 88% | 91.9% |
| Multilingualo1-preview wins | ||
| MGSM | 90% | — |
| MMLU-ProX | 86% | 81% |
| Mathematics | ||
| AIME 2023 | 94% | — |
| AIME 2024 | 96% | — |
| AIME 2025 | 95% | — |
| HMMT Feb 2023 | 90% | — |
| HMMT Feb 2024 | 92% | — |
| HMMT Feb 2025 | 91% | — |
| BRUMO 2025 | 93% | — |
| MATH-500 | 94% | — |
o1-preview is ahead overall, 72 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77% and 40.5%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 72.7. 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 64.6. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
o1-preview has the edge for reasoning in this comparison, averaging 85.4 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o1-preview has the edge for agentic tasks in this comparison, averaging 75.4 versus 50.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
o1-preview has the edge for multimodal and grounded tasks in this comparison, averaging 75.6 versus 75.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 88. Inside this category, IFEval is the benchmark that creates the most daylight between them.
o1-preview has the edge for multilingual tasks in this comparison, averaging 87.4 versus 81. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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