The Akaike information criterion (AIC) is an estimator of out-of-sample prediction error, and thus a measure of quality of statistical models, for a given set of data. Its definition is1
$$ \text{AIC} = 2d - 2\text{loglik} ,$$
where $d$ is the number of parameters estimated by the model and $\text{loglik}$ is the log of the maximized likelihood function.
1
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed). Springer.