Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
Winner · 1/8 categoriesMixtral 8x22B Instruct v0.1
36
1/8 categories1-bit Bonsai 1.7B· Mixtral 8x22B Instruct v0.1
Pick 1-bit Bonsai 1.7B 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 1.7B has the cleaner overall profile here, landing at 39 versus 36. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
1-bit Bonsai 1.7B's sharpest advantage is in reasoning, where it averages 45.1 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 1.7B.
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 1.7B | 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 1.7B wins | ||
| MuSR | 45.1% | — |
| LongBench v2 | — | 39% |
| MRCRv2 | — | 38% |
| KnowledgeMixtral 8x22B Instruct v0.1 wins | ||
| GPQA | 20.7% | — |
| MMLU | — | 77.8% |
| FrontierScience | — | 53% |
| Instruction Following | ||
| IFEval | 63% | — |
| Multilingual | ||
| MMLU-ProX | — | 42% |
| Mathematics | ||
| MATH-500 | 34.4% | — |
1-bit Bonsai 1.7B is ahead overall, 39 to 36.
Mixtral 8x22B Instruct v0.1 has the edge for knowledge tasks in this comparison, averaging 53 versus 20.7. 1-bit Bonsai 1.7B stays close enough that the answer can still flip depending on your workload.
1-bit Bonsai 1.7B has the edge for reasoning in this comparison, averaging 45.1 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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