The absolute values of the AIC scores do not matter. These scores can be negative or positive. In your example, the model with AIC=−237.847 is preferred over the model with AIC=−201.928. You should not care for the absolute values and the sign of AIC scores when comparing models.

What does it mean to have a negative AIC value?

Further more it is only meaningful to look at AIC when comparing models! But to answer your question, the lower the AIC the better, and a negative AIC indicates a lower degree of information loss than does a positive (this is also seen if you use the calculations I showed in the above answer, comparing AICs).

Is a negative AIC better?

One question students often have about AIC is: How do I interpret negative AIC values? The simple answer: The lower the value for AIC, the better the fit of the model.