There are different thermodynamic models that have been applied for modelling of asphaltene precipitation caused by various reasons, such as solvent/CO2 injection and pressure depletion. In this work, two computer codes based on two different asphaltene precipitation thermodynamic models the first being the thermodynamic micellization model with a different characterization approach and the second being the solid model have been developed and used for predicting asphaltene precipitation data reported in the literature as well as in the obtained data for Sarvak reservoir crude, which is one of the most potentially problematic Iranian heavy oil reserves under gas injection conditions. For the thermodynamic micellization model, a new approach was obtained by applying the characterization method taken from the thermodynamic solid model for oil component characterization. This new approach introduced a new matching parameter to the model, representing the interaction coefficients between asphaltene components and light hydrocarbon components, which resulted in a significant improvement in the thermodynamic micellization model predictions of asphaltene precipitation data under gas injection conditions. The model parameters obtained from a sensitivity analysis were applied in both thermodynamic models, and the experimental data of asphaltene precipitation were predicted. The asphaltene precipitation predictions from the solid model showed good agreement with the data taken under gas/solvent injection conditions. Especially for the trend of the titration curve after the peak point, reasonable agreements were observed which could rarely be found in the available literature. It has been observed that although the thermodynamic micellization model with a different characterization approach is more complex than the solid model, it is able to predict the trends of asphaltene precipitation curves for gas titration conditions reasonably well. Also, its predictions matched well with more experimental data points in comparison to the solid model predictions.