I suspect Whisper is more robust than other "SOTA" models, but this release is likely leaving a fair bit of accuracy on the table considering the amount of resources OpenAI is capable of throwing at training it.

Comparing the readily available test sets from the paper to some of my personal robust models (for the Talon models, this is greedy decoding, no language model):

                       Talon  Talon  Talon  Whisper  wav2vec 2.0
                       28M    300M   1B     Large    960h
    librispeech clean   3.21   2.52   2.40   2.7      2.7
    librispeech other   8.21   6.56   5.63   5.6      6.2
    common voice       13.88  11.65   8.86   9.5     29.9
    tedlium             7.51   6.55   5.47   4.0     10.5

I have a battery of more difficult tests on hand (including adversarial tests, and diverse accent-specific metrics). I'll look at running these tests on each of the Whisper model sizes and following up with a larger comparison.