# AIC and anova p in multilevel model, how to interpret?

I have a model with a random-nested factor, I am comparing it with a model without the random factor (to test significance of random factor) as follows:

M2<-lme(score ~ disease* time* week, random=~1|treatment/Id, method = "REML",
data = Dat1)
M3<-gls(score ~ disease * time * week, method = "REML", data = Dat1)


Comparing the two I get:

df         AIC
M2  21 -2662.715
M3  19 -2612.308


This leads me to believe M3 would be a better model (AIC closer to 0), meaning random could be taken out, or at least that M2 and M3 are different.

I check with ANOVA:

  Model   df    AIC        BIC      logLik     Test    L.Ratio    p-value
M2     1  21  -2662.715  -2550.233  1352.358
M3     2  19  -2612.308  -2510.538  1325.154   1 vs 2  54.40752   <.0001


and p is significant p<.0001.

I am not sure how to interpret this, M2 and M3 are significantly different, but does this mean random effect is a significant factor, or does it mean M3 is a better model and the therefore random effect is not significant?