GLM-5 vs Ornith-1.0-397B

Pick Ornith-1.0-397B if you want the stronger benchmark profile. GLM-5 only becomes the better choice if 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 66. 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 agentic, where it averages 77.5 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 77.5%.

GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 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 GLM-5 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. Ornith-1.0-397B gives you the larger context window at 262K, compared with 200K for GLM-5.