Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1-pro
45
0/8 categoriesQwen3.5-122B-A10B
71
Winner · 6/8 categorieso1-pro· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. o1-pro only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in coding, where it averages 76.3 against 23. The single biggest benchmark swing on the page is MMLU-ProX, 52% to 82.2%.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 200K for o1-pro.
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-pro | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticQwen3.5-122B-A10B wins | ||
| Terminal-Bench 2.0 | 40% | 49.4% |
| BrowseComp | 50% | 63.8% |
| OSWorld-Verified | 32% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| SWE-bench Pro | 23% | — |
| SWE-bench Verified | — | 72% |
| LiveCodeBench | — | 78.9% |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 48% | 76.9% |
| OfficeQA Pro | 49% | — |
| ReasoningQwen3.5-122B-A10B wins | ||
| LongBench v2 | 54% | 60.2% |
| MRCRv2 | 59% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| GPQA | 79% | 86.6% |
| FrontierScience | 63% | — |
| MMLU-Pro | — | 86.7% |
| SuperGPQA | — | 67.1% |
| Instruction Following | ||
| IFEval | — | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MMLU-ProX | 52% | 82.2% |
| Mathematics | ||
| AIME 2024 | 86% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 45. The biggest single separator in this matchup is MMLU-ProX, where the scores are 52% and 82.2%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 69.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 76.3 versus 23. o1-pro stays close enough that the answer can still flip depending on your workload.
Qwen3.5-122B-A10B has the edge for reasoning in this comparison, averaging 60.2 versus 56.3. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56 versus 39.7. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 48.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 52. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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