1-bit Bonsai 1.7B vs Grok 3 Mini

Pick Grok 3 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 or you would rather avoid the extra latency and token burn of a reasoning model.

Grok 3 Mini is clearly ahead on the aggregate, 49 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Grok 3 Mini's sharpest advantage is in knowledge, where it averages 59.8 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 66.2%.

Grok 3 Mini is also the more expensive model on tokens at $0.30 input / $0.50 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. Grok 3 Mini 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. Grok 3 Mini gives you the larger context window at 128K, compared with 32K for 1-bit Bonsai 1.7B.