Considering model averaging estimation in generalized linear models, we propose a weight choice crite- rion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is different from that for con- tinuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear mixed-effects model framework and establish associated the- ory. Numerical experiments illustrate that the proposed method is promising.