As written, SoftMax is a generalization of Logistic Regression.
Hence:

1. Performance
   If the model has more than 2 classes then you can't compare. Given K = 2 they are the same.
2. Computation Requirements
   Please explain as the computational requirements require the data, enough memory to hold it and enough time to let run.
3. Ease of Calculation of Derivatives
   The cost function is summation hence once you do it for one element you do it fol all.
4. Ease of Visualization
   Well, it is easy to visualize the Confusion Matrix even for K = 10 classes. So no issue here.
5. Cost Function
   The cost function is convex. Yet not Strictly Convex hence there infinite number of minimas.

As written, SoftMax is a generalization of Logistic Regression.

Hence:

1. Performance: If the model has more than 2 classes then you can't compare. Given K = 2 they are the same.

2. Computation Requirements: Please explain as the computational requirements require the data, enough memory to hold it and enough time to let run.

3. Ease of Calculation of Derivatives: The cost function is summation hence once you do it for one element you do it fol all.

4. Ease of Visualization: Well, it is easy to visualize the Confusion Matrix even for K = 10 classes. So no issue here.

5. Cost Function: The cost function is convex. Yet not Strictly Convex hence there infinite number of minima.