Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
1/8 categoriesGPT-5.4 nano
58
Winner · 1/8 categories1-bit Bonsai 1.7B· GPT-5.4 nano
Pick GPT-5.4 nano if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if reasoning is the priority or you want the cheaper token bill.
GPT-5.4 nano is clearly ahead on the aggregate, 58 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 nano's sharpest advantage is in knowledge, where it averages 53.2 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 82.8%. 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.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 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-5.4 nano is the reasoning model in the pair, while 1-bit Bonsai 1.7B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.4 nano gives you the larger context window at 400K, 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-5.4 nano |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 46.3% |
| OSWorld-Verified | — | 39% |
| MCP Atlas | — | 56.1% |
| Toolathlon | — | 35.5% |
| tau2-bench | — | 92.5% |
| Coding | ||
| SWE-bench Pro | — | 52.4% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 66.1% |
| MMMU-Pro w/ Python | — | 69.5% |
| OmniDocBench 1.5 | — | 0.2419 |
| Reasoning1-bit Bonsai 1.7B wins | ||
| MuSR | 45.1% | — |
| MRCRv2 | — | 38.7% |
| MRCR v2 64K-128K | — | 44.2% |
| MRCR v2 128K-256K | — | 33.1% |
| Graphwalks BFS 128K | — | 73.4% |
| Graphwalks Parents 128K | — | 50.8% |
| KnowledgeGPT-5.4 nano wins | ||
| GPQA | 20.7% | 82.8% |
| HLE | — | 37.7% |
| HLE w/o tools | — | 24.3% |
| Instruction Following | ||
| IFEval | 63% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | 34.4% | — |
GPT-5.4 nano is ahead overall, 58 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 82.8%.
GPT-5.4 nano has the edge for knowledge tasks in this comparison, averaging 53.2 versus 20.7. Inside this category, GPQA 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 38.7. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
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