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Fuel reforming during a Negative Valve Overlap (NVO) period is an effective approach to control Low Temperature Gasoline Combustion (LTGC) ignition. Previous work has shown through experiments that primary reference fuels reform easily and produce several species that drastically affect ignition characteristics. However, our previous research has been unable to accurately predict measured reformate composition at the end of the NVO period using simple single-zone models. In this work, we use a stochastic reactor model (SRM) closed cycle engine simulation to predict reformate composition accounting for in-cylinder temperature and mixture stratification. The SRM model is less computationally intensive than CFD simulations while still allowing the use of large chemical mechanisms to predict intermediate species formation rates.