1-bit Bonsai 4B vs o3-mini

Pick o3-mini if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

o3-mini is clearly ahead on the aggregate, 65 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

o3-mini's sharpest advantage is in knowledge, where it averages 70.5 against 28.7. The single biggest benchmark swing on the page is GPQA, 28.7% to 77.2%.

o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 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. o3-mini is the reasoning model in the pair, while 1-bit Bonsai 4B 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. o3-mini gives you the larger context window at 200K, compared with 32K for 1-bit Bonsai 4B.