You could try to look at the 2d example using the link and just see, how the convolution is performed in the first row.
To your use case:
You will get the same output shape, but the operation is different. since each kernel multiplies its receptive field with all input channels.
In your first example each kernel will multiply its weights with only 2 input values at two different indices, since there is only 1 input channel.
In your second example each kernel will also multiply its weights at two indices, but with all 200 input channels.
That’s why, the weight matrix is much bigger in this case.
Even though the output size is the same, the operation is quite different.