Easiest approach would be to import the SAS design into our CBC software (use the Import button from the Design tab) and use our Test Design procedure to compute the D-efficiency for the SAS design as well as the Sawtooth Software Design, holding the number of respondents constant. The Sawtooth Software design might have 300 unique blocks/versions, and the SAS design might have a lower number of blocks. You'll need to adjust the number of versions in your Sawtooth Software project specifications to match the specifications of the SAS plan.

(First, do an Export of the Sawtooth Software design, to get the right format in a .csv file. Then, simply swap in the SAS design into the .csv file and re-import into your Sawtooth Software project.)

Please recognize that the D-efficiency of a design isn't a perfect predictor for how well an experimental design will do in practice to estimate utilities with high precision. For example, the default design method in Sawtooth Software is now "Balanced Overlap" that on purpose gives up some d-efficiency for the benefit of achieving a modest degree of level overlap (level repeats within each task). Level repeating makes it so that respondents who use non-compensatory decision making (such as always picking their favorite brand or always picking the lowest price) will face a tradeoff beyond just picking their one favorite level per task.

Also, we take the approach of Balanced Overlap, because it often leads to the efficient estimation of all potential first order interaction effects. That's another benefit of Sawtooth Software's designs: they typically support all main effects plus 1st order interaction effects.

BTW, you need to raise the determinant of the X'X matrix to the -1/n power where n is the number of rows or column in the X'X matrix (covariance matrix).