Several Streptococcus pyogenes Cas9 (SpCas9) variants have been developed to improve an enzyme's specificity or to alter or broaden its protospacer-adjacent motif (PAM) compatibility, but selecting the optimal variant for a given target sequence and application remains difficult. To build computational models to predict the sequence-specific activity of 13 SpCas9 variants, we first assessed their cleavage efficiency at 26,891 target sequences. We found that, of the 256 possible four-nucleotide NNNN sequences, 156 can be used as a PAM by at least one of the SpCas9 variants. For the high-fidelity variants, overall activity could be ranked as SpCas9?≥?Sniper-Cas9?>?eSpCas9(1.1) > SpCas9-HF1?>?HypaCas9?≈?xCas9 > evoCas9, whereas their overall specificities could be ranked as evoCas9 > HypaCas9?≥?SpCas9-HF1?≈?eSpCas9(1.1) > xCas9?>?Sniper-Cas9?>?SpCas9. Using these data, we developed 16 deep-learning-based computational models that accurately predict the activity of these variants at any target sequence.