Development of numerical tools for quantitatively assessing biofuel combustion in Internal Combustion Engines and facilitating the identification of optimum operating parameters and emission strategy are challenges of engine combustion research. Biofuels obtained through e.g. a Fischer-Tropsch process (FT) are complex mixtures of wide ranges of high molecular weight hydrocarbons in the diesel and naphtha boiling range dominated by C10-C18 hydrocarbons in n-alkane, iso-alkane, alkenes, aromatic and oxygenate classes. In this paper modeling of combustion in a rapid compression machine has been performed using model compounds from a given FT biofuel distribution as surrogate fuels. Furthermore, the detailed mechanism has been reduced by applying an automatic necessity analysis removing redundant species from the detailed model. The reduced mechanisms have been optimized and evaluated for various surrogate fuels such as mixtures of nCxH2x+2, CxH2x+2−2 and CxH2x−1 for x=8, 10, 12, 14, all present in the real FT biofuel, against the full FT biofuel composition with x=10−16. The error between the occurrence of heat release peaks of surrogate fuel and FT biofuel is minimal for the proposed level of mechanism reduction indicating good ignition characteristics. Other important physical parameters for emission studies such as temperature profiles and radical production profiles are also investigated. Finally, the optimized reduced mechanism and selected surrogate fuel have also been evaluated in a more advanced combustion model, a stochastic reactor model, to further investigate the combustion characteristics and emissions under typical diesel engine conditions. The base case reduction is performed for the engine speed of 2500 rpm between −20 CAD BTDC and 60 CAD ATDC, and Pi= 2.6E6 N/m2 and Ti=860 °C. For this work the surrogate fuel consisting of nC14H30, C14H30−2 and C14H28−1 and a mechanism with 1535 species, representing almost 80% reduction, have been suggested as a reliable and practical model for further investigation.