GLM-5 vs LongCat-2.0

Pick LongCat-2.0 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

LongCat-2.0 is clearly ahead on the provisional aggregate, 80 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

LongCat-2.0's sharpest advantage is in agentic, where it averages 70.8 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 70.8%. GLM-5 does hit back in coding, so the answer changes if that is the part of the workload you care about most.

GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.75 input / $2.95 output per 1M tokens for LongCat-2.0. LongCat-2.0 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. LongCat-2.0 gives you the larger context window at 1M, compared with 200K for GLM-5.