Generalized quantifiers (e.g., “most”) allow for fine-grained statements about quantities. The discrepancy in the underlying mental representation among interpreters can affect language use and reasoning. We investigated the effect of quantifier type, quantification space (set size) and monotonicity on processing difficulty and response diversity of 77 generalized quantifiers. Shannon entropy was employed to measure response diversity. Our findings indicate: (i) Set size is a significant factor of response diversity, which implies that the underlying space is relevant for the interpretation. (ii) Quantifiers possess a rather static underlying representation within and across tasks within a participant. (iii) Quantifier type and monotonicity can affect response diversity; while the response diversity can predict response time. (iv) In reasoning, the number of generalized quantifiers versus classical quantifiers in a syllogism is a factor of response diversity. Diversity in the interpretation of generalized quantifiers may be a cause of human’s deviation from logical responses.