2021 JET experimental campaign has produced high stationary fusion power with 50%D 50%T discharges, operated with the ITER-relevant conditions i.e. operation with the baseline or hybrid scenario in the full metallic wall. It has provided a unique opportunity to assess the DT fusion power prediction capability before ITER DT experiments. This paper reports the validation of the integrated modelling codes (TRANSP, JINTRAC, and ETS) coupled with a quasilinear turbulent transport model (TGLF or QLK) against the measured data in 2021 JET DT discharges. Detailed simulation setting and the heating and transport models used are also described. The DT fusion power calculated with the interpretive TRANSP runs for 38 DT discharges agree with the measurement within 20% deviation. This indicates the additional uncertainties, that could result from the measurement error bars in kinetic profiles, impurity contents and neutron rates, and also the calculation error bars in the NBI modelling, are less than 20%. It confirms that any larger deviation of the DT fusion power prediction from the measurement could attribute to the deviation of the predicted kinetic profiles from the measurement. Without any posterior adjustment of the simulation setting, the fraction of predicted DT fusion power to the measured fusion power were found as 65% ∼ 96% for the DT baseline and 79% ∼ 97% for DT hybrid discharge. Possible sources that led to the lower DT prediction are discussed and future works to improve the fusion power prediction capability are suggested. The DT predictive modelling results have also been compared to the predictive modelling of the counterpart D discharges, where the key engineering parameters are identical. Features in the predicted kinetic profiles of the DT discharges such as underprediction of ne are also found in the prediction results of the counterpart D discharges, and it leads to similar levels of the normalized neutron rate prediction between them. This implies that the DT fusion power prediction capability could be assessed by the prediction quality of the counterpart D discharges.