Imagine having a team of programmers, each with different skill levels, working together seamlessly on a project. That's the core idea behind "model cascading" for AI code generation. Instead of relying on one large, expensive language model (LLM) to generate code, this innovative approach uses a series of smaller, more efficient models, escalating to larger models only when necessary. This research tackles the challenge of sky-high costs associated with running large AI models for code completion. The bigger the model, the better the code it generates, but the more expensive it is to run. Model cascading aims to optimize this trade-off. It works by first prompting the smallest, cheapest model in a series. This model generates not only code solutions but also test cases to evaluate its own work. If the code passes the tests with a high enough score, the process stops, saving precious computation. If not, the problem is escalated to the next larger model, and the process repeats. This continues until a satisfactory solution is found or the largest model is reached. The key innovation lies in the self-testing mechanism. Each model generates its own test cases, eliminating the need for developers to create them manually. This not only saves time and effort but also allows the system to dynamically adapt to the complexity of the task. The results are impressive. Experiments show that model cascading consistently outperforms using a single model, offering both higher accuracy and lower costs. In some cases, it can slash costs by nearly half while maintaining the same level of accuracy. This research opens exciting possibilities for making AI-powered coding tools more accessible and affordable. While the current work focuses on Python, the technique can be applied to other programming languages. Future research could explore more sophisticated cascading strategies, further optimizing the balance between cost and performance. As AI continues to transform software development, innovations like model cascading will be crucial for making these powerful tools available to everyone.