DeepSeek V4 Pro vs Ornith-1.0-397B

Pick Ornith-1.0-397B if you want the stronger benchmark profile. DeepSeek V4 Pro 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-1.0-397B is clearly ahead on the provisional aggregate, 96 to 68. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Ornith-1.0-397B's sharpest advantage is in coding, where it averages 82.4 against 58.8. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 59.1% to 77.5%.

DeepSeek V4 Pro is also the more expensive model on tokens at $1.74 input / $3.48 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ornith-1.0-397B. That is roughly Infinityx on output cost alone. Ornith-1.0-397B is the reasoning model in the pair, while DeepSeek V4 Pro 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. DeepSeek V4 Pro gives you the larger context window at 1M, compared with 262K for Ornith-1.0-397B.