GLM-5.2 vs Holo3-122B-A10B

Treat this as a split decision. GLM-5.2 makes more sense if agentic is the priority or you need the larger 1M context window; Holo3-122B-A10B is the better fit if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

GLM-5.2 and Holo3-122B-A10B finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.

GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.40 input / $3.00 output per 1M tokens for Holo3-122B-A10B. GLM-5.2 is the reasoning model in the pair, while Holo3-122B-A10B 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. GLM-5.2 gives you the larger context window at 1M, compared with 64K for Holo3-122B-A10B.