GLM-5 vs Ornith-1.0-35B

Pick Ornith-1.0-35B 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-1.0-35B finishes one point ahead on BenchLM's provisional leaderboard, 67 to 66. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.

Ornith-1.0-35B's sharpest advantage is in agentic, where it averages 64.2 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 64.2%.

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-1.0-35B. That is roughly Infinityx on output cost alone. Ornith-1.0-35B 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-1.0-35B gives you the larger context window at 262K, compared with 200K for GLM-5.