intelligent-model-router

Reduce AI model operational costs by routing subtasks to the most cost-effective model based on complexity. This approach decomposes work into planning, execution, and validation phases, using Opus for planning, Sonnet for execution, and Haiku for simple tasks to achieve 60-80% savings.

Intelligent model routing works by scoring task complexity from 0-15 and decomposing the work into planning, execution, and validation phases. It autonomously assigns Opus for planning, Sonnet for core implementation, and Haiku for simple documentation, optimizing model selection per subtask.

Use different AI models in a workflow when task complexity varies significantly within a single project. Assign Opus for complex planning, Sonnet for core implementation, and Haiku for simple documentation tasks to maintain quality while optimizing computational resource usage.

Optimize microservices architecture design costs by using phased AI model execution. The system analyzes the design request's complexity, uses Opus for the architecture planning phase, Sonnet for core implementation, and Haiku for simple documentation, ensuring efficiency at every step.

Maintain AI output quality while reducing API expenses by implementing autonomous complexity analysis and phased execution. By matching model capabilities to subtask difficulty—using Opus for heavy reasoning and Haiku for simple tasks—you achieve significant cost savings without degrading results.

Yes, this model routing approach works without external dependencies. It operates autonomously using internal scripts and references to analyze task complexity and manage the routing between Opus, Sonnet, and Haiku models for optimized execution.