Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o3-mini
65
3/8 categoriesQwen3.5-122B-A10B
71
Winner · 4/8 categorieso3-mini· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. o3-mini only becomes the better choice if reasoning is the priority.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 65. 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 54.1. The single biggest benchmark swing on the page is SWE-bench Verified, 49.3% to 72%. o3-mini does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 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 o3-mini.
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 | o3-mini | Qwen3.5-122B-A10B |
|---|---|---|
| Agentico3-mini wins | ||
| Terminal-Bench 2.0 | 67% | 49.4% |
| BrowseComp | 74% | 63.8% |
| OSWorld-Verified | 61% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| SWE-bench Verified | 49.3% | 72% |
| SWE-bench Pro | 57% | — |
| LiveCodeBench | — | 78.9% |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 73% | 76.9% |
| OfficeQA Pro | 76% | — |
| Reasoningo3-mini wins | ||
| LongBench v2 | 82% | 60.2% |
| MRCRv2 | 80% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 86.9% | — |
| GPQA | 77.2% | 86.6% |
| FrontierScience | 66% | — |
| MMLU-Pro | — | 86.7% |
| SuperGPQA | — | 67.1% |
| Instruction Followingo3-mini wins | ||
| IFEval | 93.9% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MMLU-ProX | 73% | 82.2% |
| Mathematics | ||
| AIME 2024 | 87.3% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 65. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.3% and 72%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 70.5. 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 54.1. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for reasoning in this comparison, averaging 81.1 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o3-mini has the edge for agentic tasks in this comparison, averaging 66.6 versus 56. Inside this category, Terminal-Bench 2.0 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 74.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 93.4. Inside this category, IFEval 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 73. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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