Generative artificial intelligence (AI) models are advancing rapidly, and their ability to generate code has significant implications for software development. Their use in code generation raises concerns about plagiarism, malware generation and dataset contamination. Previous work proposed methods to address these issues, using developments from the field of natural language detection. However, due to the infancy of the field, there is a deficit in established benchmarks and datasets, making a comprehensive comparison between detection methods difficult. In this work, we investigated the efficacy of five existing zero-shot detection methods on AI-generated code. To do so, we used 17 large language models to generate and analyse 113 776 code samples from six common datasets and sources. By collecting new, previously unseen data with their respective time-stamps, we address the issue of data leakage, i.e., the exposure of LLMs to testing data during pre-training. Our proposed framework enables easy integration of new LLMs and datasets, facilitating the generation, detection and analysis of AI-generated code. Following this, we examined the impact of different hyperparameters used during code generation (such as the temperature) on detection performance. Our code is available under https://github.com/Progyo/Codetector .