GLM-5 vs MiMo-V2.5

Pick GLM-5 if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if agentic is the priority or you need the larger 1M context window.

GLM-5 has the cleaner provisional overall profile here, landing at 77 versus 74. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

GLM-5's sharpest advantage is in coding, where it averages 63.2 against 56.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 65.8%. MiMo-V2.5 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.

MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-5. That is roughly Infinityx on output cost alone. MiMo-V2.5 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. MiMo-V2.5 gives you the larger context window at 1M, compared with 200K for GLM-5.