Abstract: In recent years, large Vision-Language Models (VLM) have demonstrated remarkable zero-shot prediction capabilities in image classification tasks. Existing VLM-based classification approaches typically rely on a set of predefined category labels or external textual knowledge bases as semantic prior to align and match visual content with language. However, in real-world applications, such predefined labels often fail to cover the vast and dynamic concept space of the open world, thereby limiting the adaptability and generalization ability of these models. To address this challenge, a more open-ended task, Generative Image Classification (GIC), is introduced, in which no prior semantic labels are provided, and the target categories reside in an open-ended, potentially unbounded semantic space, substantially increasing the classification difficulty. To tackle the complexity of GIC, a granularity-guided multi-model collaboration framework is proposed, designed to fully leverage the strengths of VLM in both image understanding and text generation. In this framework, prompt templates are carefully crafted to guide VLM in generating candidate labels at varying levels of semantic granularity. Subsequently, the candidate label space is expanded by a language model that associates semantically related categories based on the generated image descriptions at each granularity level. Finally, the most relevant label is identified through a semantic similarity retrieval process in the embedding space. The effectiveness and superiority of the proposed method for the GIC task are validated through extensive experiments on multiple fine-grained image classification benchmarks.