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.