Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1
63
3/8 categoriesQwen3.5-35B-A3B
67
Winner · 4/8 categorieso1· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. o1 only becomes the better choice if reasoning is the priority.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 67 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-35B-A3B's sharpest advantage is in coding, where it averages 72.6 against 46.6. The single biggest benchmark swing on the page is SWE-bench Verified, 41% to 69.2%. o1 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 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 gives you the larger context window at 262K, compared with 200K for o1.
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 | Qwen3.5-35B-A3B |
|---|---|---|
| Agentico1 wins | ||
| Terminal-Bench 2.0 | 66% | 40.5% |
| BrowseComp | 72% | 61% |
| OSWorld-Verified | 60% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| SWE-bench Verified | 41% | 69.2% |
| SWE-bench Pro | 50% | — |
| LiveCodeBench | — | 74.6% |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 68% | 75.1% |
| OfficeQA Pro | 74% | — |
| Reasoningo1 wins | ||
| LongBench v2 | 79% | 59% |
| MRCRv2 | 77% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 91.8% | — |
| GPQA | 75.7% | 84.2% |
| FrontierScience | 65% | — |
| MMLU-Pro | — | 85.3% |
| SuperGPQA | — | 63.4% |
| Instruction Followingo1 wins | ||
| IFEval | 92.2% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MMLU-ProX | 77% | 81% |
| Mathematics | ||
| AIME 2024 | 74.3% | — |
Qwen3.5-35B-A3B is ahead overall, 67 to 63. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 41% and 69.2%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 69.3. Inside this category, GPQA 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 46.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o1 has the edge for reasoning in this comparison, averaging 78.1 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 50.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multimodal and grounded tasks in this comparison, averaging 75.1 versus 70.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 91.9. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multilingual tasks in this comparison, averaging 81 versus 77. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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