This study aimed to evaluate the cost-effectiveness of different methods to determine deoxynivalenol (DON) in wheat, being non-instrumental assays (dipsticks), instrumental chemical analytical methods, predictive models, and their combinations, at collector intake. A Monte Carlo simulation model encompassing the wheat supply chain from farm field to miller intake was developed. Testing of incoming batches was done at collector intake, using one out of six scenarios for detection methods. Instrumental testing of each batch was done at miller intake. Three different thresholds for DON concentration, being 500 µg/kg, 1250 µg/kg and 1750 µg/kg, above which the batch of wheat was not accepted by the miller, were applied. The model calculates total testing costs, costs associated with non-accepted wheat batches, and total amount of non-accepted wheat, using input data for The Netherlands. Results showed that, on average, the predictive DON model, either alone or in combination with either the dipstick method or the instrumental analytical method, or no testing was most cost-effective for the entire supply chain. However, the amount of rejected wheat and related costs could be very high. Using an instrumental method resulted into the lowest amount of rejected wheat, but also into the highest chain costs. The developed model provides insight into the most cost-effective testing strategy, and division of related costs over chain actors. The use of the model could lead to better appointing (the limited) resources for food safety control.