Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesGPT-4.1 nano
44
3/8 categories1-bit Bonsai 4B· GPT-4.1 nano
Treat this as a split decision. 1-bit Bonsai 4B makes more sense if you want the cheaper token bill; GPT-4.1 nano is the better fit if reasoning is the priority or you need the larger 1M context window.
1-bit Bonsai 4B and GPT-4.1 nano finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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 nano 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 nano |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 43% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 42% |
| Coding | ||
| SWE-bench Pro | — | 18% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 53% |
| OfficeQA Pro | — | 67% |
| ReasoningGPT-4.1 nano wins | ||
| MuSR | 41.4% | — |
| LongBench v2 | — | 75% |
| MRCRv2 | — | 73% |
| KnowledgeGPT-4.1 nano wins | ||
| GPQA | 28.7% | 50.3% |
| MMLU | — | 80.1% |
| FrontierScience | — | 51% |
| Instruction FollowingGPT-4.1 nano wins | ||
| IFEval | 69.6% | 83.2% |
| Multilingual | ||
| MMLU-ProX | — | 59% |
| Mathematics | ||
| MATH-500 | 65.8% | — |
| AIME 2024 | — | 9.8% |
1-bit Bonsai 4B and GPT-4.1 nano are tied on overall score, so the right pick depends on which category matters most for your use case.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 41.4. 1-bit Bonsai 4B stays close enough that the answer can still flip depending on your workload.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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