Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
2/8 categoriesMiMo-V2-Omni
~75
Winner · 0/8 categoriesGemma 4 31B· MiMo-V2-Omni
Pick MiMo-V2-Omni if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if coding is the priority.
MiMo-V2-Omni has the cleaner overall profile here, landing at 75 versus 73. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MiMo-V2-Omni gives you the larger context window at 262K, compared with 256K for Gemma 4 31B.
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 | Gemma 4 31B | MiMo-V2-Omni |
|---|---|---|
| Agentic | ||
| Claw-Eval | — | 56.7% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | — |
| SWE-bench Verified | — | 74.8% |
| Multimodal & GroundedGemma 4 31B wins | ||
| MMMU-Pro | 76.9% | 76.8% |
| Reasoning | ||
| BBH | 74.4% | — |
| MRCRv2 | 66.4% | — |
| Knowledge | ||
| GPQA | 84.3% | — |
| MMLU-Pro | 85.2% | — |
| HLE | 26.5% | — |
| HLE w/o tools | 19.5% | — |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| Coming soon | ||
MiMo-V2-Omni is ahead overall, 75 to 73. The biggest single separator in this matchup is MMMU-Pro, where the scores are 76.9% and 76.8%.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 74.8. MiMo-V2-Omni stays close enough that the answer can still flip depending on your workload.
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 76.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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