Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
Winner · 1/8 categoriesMixtral 8x22B Instruct v0.1
36
1/8 categories1-bit Bonsai 4B· Mixtral 8x22B Instruct v0.1
Pick 1-bit Bonsai 4B if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if knowledge is the priority or you need the larger 64K context window.
1-bit Bonsai 4B is clearly ahead on the aggregate, 44 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
1-bit Bonsai 4B's sharpest advantage is in reasoning, where it averages 41.4 against 38.5. Mixtral 8x22B Instruct v0.1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Mixtral 8x22B Instruct v0.1 gives you the larger context window at 64K, compared with 32K for 1-bit Bonsai 4B.
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 4B | Mixtral 8x22B Instruct v0.1 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 35% |
| BrowseComp | — | 32% |
| OSWorld-Verified | — | 28% |
| Coding | ||
| HumanEval | — | 54.8% |
| SWE-bench Pro | — | 40% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 35% |
| OfficeQA Pro | — | 36% |
| Reasoning1-bit Bonsai 4B wins | ||
| MuSR | 41.4% | — |
| LongBench v2 | — | 39% |
| MRCRv2 | — | 38% |
| KnowledgeMixtral 8x22B Instruct v0.1 wins | ||
| GPQA | 28.7% | — |
| MMLU | — | 77.8% |
| FrontierScience | — | 53% |
| Instruction Following | ||
| IFEval | 69.6% | — |
| Multilingual | ||
| MMLU-ProX | — | 42% |
| Mathematics | ||
| MATH-500 | 65.8% | — |
1-bit Bonsai 4B is ahead overall, 44 to 36.
Mixtral 8x22B Instruct v0.1 has the edge for knowledge tasks in this comparison, averaging 53 versus 28.7. 1-bit Bonsai 4B stays close enough that the answer can still flip depending on your workload.
1-bit Bonsai 4B has the edge for reasoning in this comparison, averaging 41.4 versus 38.5. Mixtral 8x22B Instruct v0.1 stays close enough that the answer can still flip depending on your workload.
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