Probabilistic models to quantify context effects in speech recognition have proven their value in audiology. Boothroyd and Nittrouer [J. Acoust. Soc. Am. 84, 101-114 (1988)] introduced a model with the j-factor and k-factor as context parameters. Later, Bronkhorst, Bosman, and Smoorenburg [J. Acoust. Soc. Am. 93, 499-509 (1993)] proposed an elaborated mathematical model to quantify context effects. The present study explores existing models and proposes a new model to quantify the effect of context in sentence recognition. The effect of context is modeled by parameters that represent the change in the probability that a certain number of words in a sentence are correctly recognized. Data from two studies using a Dutch sentence-in-noise test were analyzed. The most accurate fit was obtained when using signal-to-noise ratio-dependent context parameters. Furthermore, reducing the number of context parameters from five to one had only a small effect on the goodness of fit for the present context model. An analysis of the relationships between context parameters from the different models showed that for a change in word recognition probability, the different context parameters can change in opposite directions, suggesting opposite effects of sentence context. This demonstrates the importance of controlling for the recognition probability of words in isolation when comparing the use of sentence context between different groups of listeners.