GLM-4.7 vs GPT-4.1 mini

Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if knowledge is the priority or you need the larger 1M context window.

GLM-4.7 is clearly ahead on the provisional aggregate, 70 to 46. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 23.6%. GPT-4.1 mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 is the reasoning model in the pair, while GPT-4.1 mini 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. GPT-4.1 mini gives you the larger context window at 1M, compared with 200K for GLM-4.7.