You certainly may! AIC is not really an absolute measure, it's intended for model comparison, and is a relative measure.
For example, AIC in a normal linear model is driven by the variance of the errors. If the entire problem gets rescaled by, for example, dividing the target variable by 100, the AIC will change, but the relative magnitude of AIC with and without an extra variable will not:
> # Generate random example
> x <- rnorm(100)
> z <- rnorm(100)
> y <- x + rnorm(100)
> # Base case
> AIC(lm(y~x), lm(y~x+z))
lm(y ~ x) 3 290.7473
lm(y ~ x + z) 4 292.7220
> # Rescale y
> AIC(lm(y/100~x), lm(y/100~x+z))
lm(y/100 ~ x) 3 -630.2867
lm(y/100 ~ x + z) 4 -628.3121
> # Rescale y again
> AIC(lm(y*10000~x), lm(y*10000~x+z))
lm(y * 10000 ~ x) 3 2132.815
lm(y * 10000 ~ x + z) 4 2134.790
So your comparison comes out the same regardless of the -2LL part.
Note also that the difference between the AICs in each of the three examples is exactly the same, except for rounding in the last digit. You can think of it as that, because we are using the log likelihood, the "scale" of the problem becomes an additive term, and just serves to move the log likelihood up and down the real number line; all the models move the same distance (because the term is additive) so preserve the relative differences between them. A loose explanation, admittedly.