Part III - Python Examples for Switching Between Models

In the upcoming chapters, we explore how to use a unified LLM interface that balances cost, performance, and local sovereignty by using the libraries LiteLLM and RouteLLM. LiteLLM provides a consistent completion function to interact with both high-end cloud providers like OpenAI, Claude, and Gemini and also local instances via Ollama. RouteLLM acts as an intelligent gateway: by configuring a “Strong” model (GPT-4) alongside a “Weak” model (Qwen 3 running locally), the script utilizes a Matrix Factorization (MF) router to dynamically assign tasks to different models based on the complexity of user prompts. This architecture allows developers to define a THRESHOLD value, effectively a dial to tune the system’s preference for quality versus economy—ensuring that simple queries stay local while sophisticated reasoning tasks are routed to either the cloud or stronger local models, all while maintaining a singular, standardized codebase for model interaction.