The mechanistic approach and the potential predictive power of Dynamic Energy Budget (DEB) models are making this modelling framework increasingly popular in aquaculture management. The potential to mechanistically simulate life-history traits of a species in a changing environment has already been applied to many commercial species, including the New Zealand Greenshell™ mussel Perna canaliculus. However, the previous P. canaliculus model parameterisation, available in the Add-my-Pet (AmP) collection, has been carried out on a limited data set. We used data obtained through targeted laboratory experiments and combined it with literature data, to derive a new version of the model which included information on additional processes – such as feeding, respiration, and reproduction – and life stages: larvae and adults. We compared the performance of the previous version of the model, termed AmP-2018 with the revised model (AmP-2024), to predict the whole life-cycle of the mussel and life-history traits of interest in the context of aquaculture. The entry completeness score (0−10), which provides an indication of data quality and variety as well as model comprehensiveness and applicability, increased dramatically from 2.5 to 5.5, suggesting P. canaliculus is now the most comprehensively DEB-parameterised bivalve, while the model increased in accuracy of the predictions. The endeavour highlights several conclusions: (i) reliable temperature response data are required to establish the thermal response curve; the presence/absence of this type of data should be taken into account when assessing the completeness of a model and its applicability to predict thermal responses of a species; (ii) species-specific parameter values are preferred over general (higher taxonomic level) specific values, e.g. for tissue organics and water content, as they increase the accuracy of predictions; (iii) data pertaining to laboratory trials and to wild populations both need to be taken into account when calibrating a mechanistic model at a species level; and (iv) calculating energy requirements of any organism, including bivalves, requires data on characteristics and products often overlooked in an experimental setting. We discuss the new version of the model in the context of the knowledge and predictive power potentially gained by adding more information and data during model calibration. The obtained results and conclusions are especially relevant in aquaculture, whether for commercial rearing of mussels, or their potential application in multitrophic aquaculture systems.