Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiMo-V2-Flash
67
3/8 categoriesQwen3.5-27B
71
Winner · 4/8 categoriesMiMo-V2-Flash· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. MiMo-V2-Flash only becomes the better choice if agentic is the priority.
Qwen3.5-27B is clearly ahead on the aggregate, 71 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 63.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63% to 41.6%. MiMo-V2-Flash does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-27B gives you the larger context window at 262K, compared with 256K for MiMo-V2-Flash.
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-Flash | Qwen3.5-27B |
|---|---|---|
| AgenticMiMo-V2-Flash wins | ||
| Terminal-Bench 2.0 | 63% | 41.6% |
| BrowseComp | 65% | 61% |
| OSWorld-Verified | 58% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 84.8% | — |
| SWE-bench Verified | 73.4% | 72.4% |
| LiveCodeBench | 80.6% | 80.7% |
| SWE-bench Pro | 52% | — |
| SWE Multilingual | 71.7% | — |
| Multimodal & GroundedMiMo-V2-Flash wins | ||
| MMMU-Pro | 78% | 75% |
| OfficeQA Pro | 73% | — |
| ReasoningMiMo-V2-Flash wins | ||
| MuSR | 74% | — |
| BBH | 85% | — |
| LongBench v2 | 60.6% | 60.6% |
| MRCRv2 | 73% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 86.7% | — |
| GPQA | 83.7% | 85.5% |
| SuperGPQA | 76% | 65.6% |
| MMLU-Pro | 84.9% | 86.1% |
| HLE | 14% | — |
| FrontierScience | 71% | — |
| SimpleQA | 76% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 84% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MGSM | 83% | — |
| MMLU-ProX | 77% | 82.2% |
| Mathematics | ||
| AIME 2023 | 79% | — |
| AIME 2024 | 81% | — |
| AIME 2025 | 94.1% | — |
| HMMT Feb 2023 | 75% | — |
| HMMT Feb 2024 | 77% | — |
| HMMT Feb 2025 | 76% | — |
| BRUMO 2025 | 78% | — |
| MATH-500 | 90% | — |
Qwen3.5-27B is ahead overall, 71 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63% and 41.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 63.7. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 67.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
MiMo-V2-Flash has the edge for reasoning in this comparison, averaging 68.3 versus 60.6. Qwen3.5-27B stays close enough that the answer can still flip depending on your workload.
MiMo-V2-Flash has the edge for agentic tasks in this comparison, averaging 61.8 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
MiMo-V2-Flash has the edge for multimodal and grounded tasks in this comparison, averaging 75.8 versus 75. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 79.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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