Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiMo-V2-Omni
~75
Winner · 1/8 categoriesQwen3.5-27B
71
1/8 categoriesMiMo-V2-Omni· Qwen3.5-27B
Pick MiMo-V2-Omni if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if coding is the priority.
MiMo-V2-Omni is clearly ahead on the aggregate, 75 to 71. 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 multimodal & grounded, where it averages 76.8 against 75. The single biggest benchmark swing on the page is SWE-bench Verified, 74.8% to 72.4%. Qwen3.5-27B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
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 | Qwen3.5-27B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 41.6% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| SWE-bench Verified | 74.8% | 72.4% |
| LiveCodeBench | — | 80.7% |
| Multimodal & GroundedMiMo-V2-Omni wins | ||
| MMMU-Pro | 76.8% | 75% |
| Reasoning | ||
| LongBench v2 | — | 60.6% |
| Knowledge | ||
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| GPQA | — | 85.5% |
| Instruction Following | ||
| IFEval | — | 95% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| Coming soon | ||
MiMo-V2-Omni is ahead overall, 75 to 71. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 74.8% and 72.4%.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 74.8. 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. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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