Current feed formulation and evaluation practices rely on static values for the nutritional content of feed ingredients and assume additivity. Hereby the complex interplay among nutrients in the diet and the dynamic digestive processes are ignored. To improve future predictions of diet nutritional value requires acknowledging nutrient digestion kinetics and diet × animal interactions. An in silico nutrient-based mechanistic digestion model for growing pigs called "SNAPIG" (Simulating Nutrient digestion and Absorption kinetics in PIGs) was developed. Aiming to predict the rate and extent of nutrient absorption from diets varying in ingredient origins and physicochemical properties, the model represents digestion kinetics of ingested protein, starch, fat, and non-starch polysaccharides, through passage, hydrolysis, absorption, and endogenous secretions of nutrients along the stomach, proximal small intestine, distal small intestine, and caecum + colon. Input variables are nutrient intake and their physicochemical properties (i.e. solubility, degradability). Rate and extent of starch and protein hydrolysis per digestive segment were derived from in vitro assays according to their feed ingredient origin. Passage of digesta from the stomach was modelled as a function of feed intake level, dietary nutrient solubility and diet viscosity. Model evaluation included testing against independent data from in vivo studies on nutrient appearance in (portal) blood of growing pigs. Simulating diets varying in physicochemical properties and ingredient-origins resulted for glucose absorption in predicted vs. observed time of peak (TOP) 44±15 vs. 54±20 min±SD (RMSPE = 39%, disturbance error ED = 65%; concordance correlation coefficient CCC = 0.38) and area under curve (AUC) 69±30 vs. 63±20 % (RMSPE = 39%, ED = 42%, CCC = 0.58), for amino acids (AA) TOP 61±11 vs. 58±34 min (RMSPE = 60%, ED = 96%, CCC = 0.09) and AUC 83±2 vs. 63±13 % (RMSPE = 40%, ED = 27%, CCC = -0.02), and for apparent ileal protein digestibility 78±5 vs. 70±5% (RMSPE = 12%, ED = 12%, CCC = 0.34) and fat (86±4 vs. 82±15%, RMSPE = 16%, ED = 86%, CCC = 0.27). Further model improvements requires a dataset covering portal nutrient appearance studies preferably combined with digestion kinetics in pigs fed diets varying in ingredient origin. Despite limitations, the model enhances our quantitative understanding of nutrient digestion kinetics as affected by diets varying in ingredient origins and physicochemical properties. Already useful as research tool, SNAPIG can be coupled with a metabolic model to aid future predictions of dietary and feeding-strategies impacts on pig’s growth.