Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesGPT-4.1 mini
56
Winner · 3/8 categories1-bit Bonsai 4B· GPT-4.1 mini
Pick GPT-4.1 mini if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you want the cheaper token bill.
GPT-4.1 mini is clearly ahead on the aggregate, 56 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 mini's sharpest advantage is in reasoning, where it averages 80.9 against 41.4. The single biggest benchmark swing on the page is GPQA, 28.7% to 64.2%.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 4B. That is roughly Infinityx on output cost alone. GPT-4.1 mini gives you the larger context window at 1M, 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 | GPT-4.1 mini |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 54% |
| BrowseComp | — | 71% |
| OSWorld-Verified | — | 49% |
| Coding | ||
| SWE-bench Verified | — | 23.6% |
| SWE-bench Pro | — | 30% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 66% |
| OfficeQA Pro | — | 74% |
| ReasoningGPT-4.1 mini wins | ||
| MuSR | 41.4% | — |
| LongBench v2 | — | 80% |
| MRCRv2 | — | 82% |
| KnowledgeGPT-4.1 mini wins | ||
| GPQA | 28.7% | 64.2% |
| MMLU | — | 87.5% |
| FrontierScience | — | 61% |
| Instruction FollowingGPT-4.1 mini wins | ||
| IFEval | 69.6% | 88.5% |
| Multilingual | ||
| MMLU-ProX | — | 72% |
| Mathematics | ||
| MATH-500 | 65.8% | — |
| AIME 2024 | — | 23.1% |
GPT-4.1 mini is ahead overall, 56 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 64.2%.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.3 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for reasoning in this comparison, averaging 80.9 versus 41.4. 1-bit Bonsai 4B stays close enough that the answer can still flip depending on your workload.
GPT-4.1 mini has the edge for instruction following in this comparison, averaging 88.5 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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