Resource leaks, which are defects caused by the failure to timely and properly close the limited system resources, are widely present in programs of various languages and possess a certain degree of concealment. The traditional defect detection methods usually predict the resource leaks in software based on rules and heuristic search. In recent years, defect detection methods based on deep learning have captured the semantic information in the code through different code representation forms and by using techniques such as recurrent neural networks and graph neural networks. Recent studies show that language models have performed outstandingly in tasks such as code understanding and generation. However, the advantages and limitations of large language models (LLMs) in the specific task of resource leak detection have not been fully evaluated. The effectiveness of the detection methods based on traditional models, small models, and LLMs in the task of resource leak detection is studied, and various improvement methods such as few-shot learning, fine-tuning and the combination of static analysis and LLMs are explored. Specifically, taking the JLeaks and DroidLeaks datasets as the experimental objects, the performance of different models is analyzed from multiple dimensions such as the root causes of resource leaks, resource types and code complexity. The experimental results show that the fine-tuning technique can significantly improve the detection effect of LLMs in the field of resource leak detection. However, most models still need to be improved in identifying the resource leaks caused by third-party libraries. In addition, the code complexity has a greater influence on the detection methods based on traditional models for resource leak detection.