Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
Winner · 0/8 categoriesGranite-4.0-1B
~40
2/8 categories1-bit Bonsai 4B· Granite-4.0-1B
Pick 1-bit Bonsai 4B if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if instruction following is the priority or you need the larger 128K context window.
1-bit Bonsai 4B is clearly ahead on the aggregate, 44 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Granite-4.0-1B gives you the larger context window at 128K, 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 | Granite-4.0-1B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | — | 73% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| MuSR | 41.4% | — |
| BBH | — | 59.7% |
| KnowledgeGranite-4.0-1B wins | ||
| GPQA | 28.7% | 29.7% |
| MMLU | — | 59.7% |
| MMLU-Pro | — | 32.9% |
| Instruction FollowingGranite-4.0-1B wins | ||
| IFEval | 69.6% | 78.5% |
| Multilingual | ||
| MGSM | — | 27.5% |
| Mathematics | ||
| MATH-500 | 65.8% | — |
1-bit Bonsai 4B is ahead overall, 44 to 40. The biggest single separator in this matchup is IFEval, where the scores are 69.6% and 78.5%.
Granite-4.0-1B has the edge for knowledge tasks in this comparison, averaging 31.7 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Granite-4.0-1B has the edge for instruction following in this comparison, averaging 78.5 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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