Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesGPT-4o mini
55
Winner · 2/8 categories1-bit Bonsai 1.7B· GPT-4o mini
Pick GPT-4o mini if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if you want the cheaper token bill.
GPT-4o mini is clearly ahead on the aggregate, 55 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o mini's sharpest advantage is in knowledge, where it averages 62 against 20.7.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 1.7B. That is roughly Infinityx on output cost alone. GPT-4o mini gives you the larger context window at 128K, 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 | GPT-4o mini |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 58% |
| BrowseComp | — | 49% |
| OSWorld-Verified | — | 44% |
| Coding | ||
| HumanEval | — | 87.2% |
| SWE-bench Pro | — | 65% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 66% |
| OfficeQA Pro | — | 53% |
| ReasoningGPT-4o mini wins | ||
| MuSR | 45.1% | — |
| LongBench v2 | — | 49% |
| MRCRv2 | — | 50% |
| KnowledgeGPT-4o mini wins | ||
| GPQA | 20.7% | — |
| MMLU | — | 82% |
| FrontierScience | — | 62% |
| Instruction Following | ||
| IFEval | 63% | — |
| Multilingual | ||
| MGSM | — | 87% |
| MMLU-ProX | — | 68% |
| Mathematics | ||
| MATH-500 | 34.4% | — |
GPT-4o mini is ahead overall, 55 to 39.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 62 versus 20.7. 1-bit Bonsai 1.7B stays close enough that the answer can still flip depending on your workload.
GPT-4o mini has the edge for reasoning in this comparison, averaging 49.5 versus 45.1. 1-bit Bonsai 1.7B stays close enough that the answer can still flip depending on your workload.
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