One of the main factors for this is probably due to dataset size. Commercial STT models are trained on 10s of thousands of hours of data from real-world data. Even a decent model architecture is going to perform pretty well on that much data.

Most open source models are trained on Libri, SWB, etc. which are not really big or diverse enough for real-world scenarios.

But to max-out results the devil is in the details IMO (network architecture, optimizer, weight initialization, regularization, data augmentation, hyperparam tuning, etc) which requires a lot of experiments.