In this work, the current state-of-the-art of articulatory speech synthesis (VOCALTRACTLAB) is compared to a wide range of different text-to-speech systems that once represented or still represent the continuously evolving state-of-the-art of speech synthesis technology. The comparison systems include neural and concatenative synthesis by Google and Microsoft, as well as Hidden Markov Model-based, unit-selection and diphone synthesis developed at universities (using MARYTTS, MBROLA and DRESS). A small corpus of 15 German sentences was synthesized using the text-to-speech (and, if available, re-synthesis) functionalities of each system. The intelligibility of the synthesized utterances was evaluated in an ASR experiment. The naturalness of the utterances was evaluated in a multi-stimulus Likert test by 50 German native speakers. As an additional reference, recordings of natural speech were used in the experiments. It was found that the articulatory synthesis can achieve a performance on par with the non-commercial synthesis systems in terms of intelligibility and naturalness, while being significantly outperformed by the commercial synthesis systems.