GLM-5 vs Sakana Fugu Ultra

Pick Sakana Fugu Ultra if you want the stronger benchmark profile. GLM-5 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.

Sakana Fugu Ultra is clearly ahead on the provisional aggregate, 100 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Sakana Fugu Ultra's sharpest advantage is in agentic, where it averages 82.1 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 82.1%.

Sakana Fugu Ultra is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 9.4x on output cost alone. Sakana Fugu Ultra 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. Sakana Fugu Ultra gives you the larger context window at 1M, compared with 200K for GLM-5.