different aic and LL in glmer(lme4) and glimmix(SAS)?

The difference is probably due to the way that the deviance is defined
for the binomial family in R. ?A glm family object is a list of
functions and expressions. ?One of the functions, called "dev.resids"
has arguments y, mu and weights. ?You can specify the response for a
binomial family as the 0/1 responses or as a matrix with two columns
as you did here. ?When you use the two column specification the
response y is transformed to the fraction of successes and the number
of cases is incorporated in the weights. ?It turns out that this is
all the information necessary for obtaining the mle's of the
parameters but it does not give the same deviance as you would get by
listing the 0/1 responses.

Isn't it also possible the difference is because (e.g.) lme4 drops
constants out of the likelihood and SAS doesn't?