Recently, deep learning frameworks have been shown to learn a feature embedding that captures fine-grained image similarity using image triplets or quadruplets that consider pairwise relationships between image pairs. In real-world datasets, a class contains fine-grained categorization that exhibits within-class variability. In such a scenario, these frameworks fail to learn the relative ordering between - (i) samples belonging to the same category, (ii) samples from a different category within a class and (iii) samples belonging to a different class. In this paper, we propose the quadlet loss function, that learns an order-preserving fine-grained image similarity by learning through quadlets (query:q, positive:p, intermediate:i, negative:n) where p is sampled from the same category as q, i belongs to a fine-grained category within the class of q and n is sampled from a different class than that of q. We propose a deep quadlet network to learn the feature embedding using the quadlet loss function. We present an extensive evaluation of our proposed ranking model against state-of-the-art baselines on three datasets with fine-grained categorization. The results show significant improvement over the baselines for both order-preserving fine-grained ranking task and general image ranking task.