Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiMo-V2-Omni
~75
Winner · 1/8 categoriesQwen3.6 Plus
69
1/8 categoriesMiMo-V2-Omni· Qwen3.6 Plus
Pick MiMo-V2-Omni if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if multimodal & grounded is the priority or you need the larger 1M context window.
MiMo-V2-Omni is clearly ahead on the aggregate, 75 to 69. 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 64.9. The single biggest benchmark swing on the page is SWE-bench Verified, 74.8% to 78.8%. Qwen3.6 Plus does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Qwen3.6 Plus gives you the larger context window at 1M, compared with 262K for MiMo-V2-Omni.
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 | Qwen3.6 Plus |
|---|---|---|
| Agentic | ||
| Claw-Eval | 56.7% | 58.7% |
| Terminal-Bench 2.0 | — | 61.6% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| DeepPlanning | — | 41.5% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| OSWorld-Verified | — | 62.5% |
| CodingMiMo-V2-Omni wins | ||
| SWE-bench Verified | 74.8% | 78.8% |
| SWE-bench Pro | — | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| MMMU-Pro | 76.8% | 78.8% |
| MMMU | — | 86.0% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| Reasoning | ||
| AI-Needle | — | 68.3% |
| LongBench v2 | — | 62% |
| Knowledge | ||
| GPQA | — | 90.4% |
| SuperGPQA | — | 71.6% |
| MMLU-Pro | — | 88.5% |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| HLE | — | 28.8% |
| Instruction Following | ||
| IFEval | — | 94.3% |
| IFBench | — | 74.2% |
| Multilingual | ||
| MMLU-ProX | — | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
MiMo-V2-Omni is ahead overall, 75 to 69. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 74.8% and 78.8%.
MiMo-V2-Omni has the edge for coding in this comparison, averaging 74.8 versus 64.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multimodal and grounded tasks in this comparison, averaging 78.8 versus 76.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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