Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiMo-V2-Omni
~75
Winner · 2/8 categoriesQwen3.5-35B-A3B
67
0/8 categoriesMiMo-V2-Omni· Qwen3.5-35B-A3B
Pick MiMo-V2-Omni if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
MiMo-V2-Omni is clearly ahead on the aggregate, 75 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2-Omni's sharpest advantage is in coding, where it averages 74.8 against 72.6. The single biggest benchmark swing on the page is SWE-bench Verified, 74.8% to 69.2%.
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 | MiMo-V2-Omni | Qwen3.5-35B-A3B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40.5% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 54.5% |
| tau2-bench | — | 81.2% |
| CodingMiMo-V2-Omni wins | ||
| SWE-bench Verified | 74.8% | 69.2% |
| LiveCodeBench | — | 74.6% |
| Multimodal & GroundedMiMo-V2-Omni wins | ||
| MMMU-Pro | 76.8% | 75.1% |
| Reasoning | ||
| LongBench v2 | — | 59% |
| Knowledge | ||
| MMLU-Pro | — | 85.3% |
| SuperGPQA | — | 63.4% |
| GPQA | — | 84.2% |
| Instruction Following | ||
| IFEval | — | 91.9% |
| Multilingual | ||
| MMLU-ProX | — | 81% |
| Mathematics | ||
| Coming soon | ||
MiMo-V2-Omni is ahead overall, 75 to 67. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 74.8% and 69.2%.
MiMo-V2-Omni has the edge for coding in this comparison, averaging 74.8 versus 72.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
MiMo-V2-Omni has the edge for multimodal and grounded tasks in this comparison, averaging 76.8 versus 75.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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