This research introduces a cascaded multi-model framework that optimizes the cost-accuracy tradeoff in code completion tasks by strategically routing requests through models of different sizes.

• Creates a dynamic selection system that analyzes input complexity to determine which model to use
• Implements self-testing algorithms to verify code quality without human intervention
• Achieves up to 40% cost reduction while maintaining comparable accuracy to larger models
• Adapts to varying server needs and computational constraints in real-world scenarios

For engineering teams, this framework offers a practical solution to deploy high-quality code assistance tools without overwhelming computational resources, making advanced AI coding assistance more accessible and economically viable.

Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing