GPT-4.1 mini vs Ornith-1.0-9B

Pick Ornith-1.0-9B if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.

Ornith-1.0-9B is clearly ahead on the provisional aggregate, 52 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Ornith-1.0-9B's sharpest advantage is in coding, where it averages 69.4 against 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 23.6% to 69.4%.

GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ornith-1.0-9B. That is roughly Infinityx on output cost alone. Ornith-1.0-9B is the reasoning model in the pair, while GPT-4.1 mini 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-4.1 mini gives you the larger context window at 1M, compared with 262K for Ornith-1.0-9B.