llm-router

To optimize LLM costs, route cheap tasks like intent classification to Haiku or GPT-4o-mini, and complex reasoning tasks to Sonnet, Opus, or o1. This model selection strategy achieves 45-85% cost savings by matching task complexity to the appropriate model tier.

LLM model routing is the process of dynamically selecting the optimal LLM provider and model for each task based on complexity, cost budget, and capability requirements. You need it when deciding which model to call in multi-model agent systems to avoid over-reliance on expensive models.

Select the best LLM model by evaluating task complexity against your cost budget and capability requirements. Route simple tasks to cheaper models like Haiku and reserve expensive models like Opus for complex code generation or deep reasoning tasks.

Yes, model routing handles both simple classification and complex code generation by dynamically assigning tasks to appropriate LLM tiers. Classification routes to cheaper models, while code generation routes to more capable models, optimizing overall performance and cost.

You should not use automated model selection for prompt engineering, model fine-tuning, or training custom models. This routing approach is specifically designed for optimizing LLM costs and selecting between existing models based on task complexity and budget constraints.