How to express complexity of two functions considering it is the same in big O notation
Big O notation is oblivious to this difference. This is one of its downsides.
One reason we use big O notation in the first place is that the exact running time depends on the system used to run the algorithms. It is meaningless to say that a given algorithm takes X instructions, since X depends on the CPU, the compiler, and so on. However, in some cases we can find a proxy for the running time which is meaningful to look at.
The prototypical example is the number of comparisons in a comparison-based sorting algorithm. In many cases, number of comparisons is a good proxy for the overall running time, and this is a &quot;dimensionless&quot; quantity which is meaningful to compare across algorithms.
Another option is to implement your algorithms as efficiently as possible, and then compare their running time empirically. In a practical paper, this is a very reasonable thing to do.
In your particular case, you can just say that algorithm B invokes algorithm A three times, and consequently is three times slower. This is not reflected in big O notation, but is quite precise and meaningful.