AIC

Just to expand a little on Hossein's answer:

AIC is a measure of relative goodness of fit. If you take a model and calculate its AIC then you might get a value of, say, 2000. That number on its own is meaningless, and tells you nothing about how well your model fits. However, say you then fit another model which contains one more explanatory variable. When you calculate the AIC again, you see that it is dropped to 1500. That is now evidence that model 2 is a better fit to the data than model 1.

AIC is useful for comparing models, but it does not tell you anything about the goodness of fit of a single, isolated model.