It depends. The model implementations in GARLI are intentionally identical to those in PAUP, so in general the scores should be quite close, although PAUP* does more intensive optimization. If you’ve run GARLI for sufficiently long and not played with the optimization settings, the score will probably be within a few tenths of a log-likelihood unit from the score one would get optimizing in PAUP*.
On very large trees it may be somewhat more. In some very rare conditions the score given by GARLI is better than that given by PAUP* after optimization, which appears to be due to PAUP* getting trapped in local branch-length optima. This should not be cause for concern.
If you want to be absolutely sure of the lnL score of a tree inferred by GARLI, optimize it in PAUP*. More.
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