Does Sampling size matters in Multi classification Model
It is always better to keep sample sizes close each other. The problem you are facing is Imbalanced Classification. 
There are lots of methods you can apply such as upsampling/downsampling, synthetic data generation (check SMOTE).

Model:

I would first convert the model to binary classification such that:


model 1 predicts: A or not A
model2 predicts B or not B in "not A" group


Note: Another option three x or not x classifier for three classes)

Then I would apply those sampling techniques (see above) to models. If you have high number of observations try to use downsampling to make class weights as 50:50, if you cant do that try SMOTE to generate data from minority class. 

Metrics:

Note that if you don't have balanced dataset, try not to use accuracy as a performance metric. Consider area under ROC, F1 and precision/recall according to your case. 

Hope it helps!