Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesMiMo-V2-Omni
~75
Winner · 0/8 categories1-bit Bonsai 8B· MiMo-V2-Omni
Benchmark data for 1-bit Bonsai 8B and MiMo-V2-Omni is coming soon on BenchLM.
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
MiMo-V2-Omni has the larger context window at 262K, compared with 64K for 1-bit Bonsai 8B.
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 | 1-bit Bonsai 8B | MiMo-V2-Omni |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| SWE-bench Verified | — | 74.8% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 76.8% |
| Reasoning | ||
| MuSR | 50% | — |
| Knowledge | ||
| GPQA | 30% | — |
| Instruction Following | ||
| IFEval | 79.8% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | 66% | — |
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
1-bit Bonsai 8B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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