Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Sibling matchup inside the 1-bit Bonsai family.
1-bit Bonsai 1.7B
~39
1/8 categories1-bit Bonsai 4B
~44
Winner · 3/8 categories1-bit Bonsai 1.7B· 1-bit Bonsai 4B
1-bit Bonsai 1.7B makes more sense if reasoning is the priority, while 1-bit Bonsai 4B is the cleaner fit if mathematics is the priority.
1-bit Bonsai 1.7B and 1-bit Bonsai 4B sit in the same 1-bit Bonsai family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
1-bit Bonsai 4B is clearly ahead on the aggregate, 44 to 39. 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 mathematics, where it averages 65.8 against 34.4. The single biggest benchmark swing on the page is MATH-500, 34.4% to 65.8%. 1-bit Bonsai 1.7B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
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 | 1-bit Bonsai 4B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| Coming soon | ||
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning1-bit Bonsai 1.7B wins | ||
| MuSR | 45.1% | 41.4% |
| Knowledge1-bit Bonsai 4B wins | ||
| GPQA | 20.7% | 28.7% |
| Instruction Following1-bit Bonsai 4B wins | ||
| IFEval | 63% | 69.6% |
| Multilingual | ||
| Coming soon | ||
| Mathematics1-bit Bonsai 4B wins | ||
| MATH-500 | 34.4% | 65.8% |
1-bit Bonsai 1.7B and 1-bit Bonsai 4B are sibling variants in the 1-bit Bonsai family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. 1-bit Bonsai 4B is ahead overall 44 to 39.
1-bit Bonsai 4B has the edge for knowledge tasks in this comparison, averaging 28.7 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
1-bit Bonsai 4B has the edge for math in this comparison, averaging 65.8 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
1-bit Bonsai 1.7B has the edge for reasoning in this comparison, averaging 45.1 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
1-bit Bonsai 4B has the edge for instruction following in this comparison, averaging 69.6 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.