📝Multi-Model Orchestration is the practice of coordinating several different AI models within one workflow, assigning each task to the model whose capability, speed, or cost fits it best.
Models differ in meaningful ways. Frontier models reason better, smaller models answer faster and more cheaply, and models from different providers have distinct strengths in coding, writing, vision, or long context. An orchestration layer decides which model handles each step and passes results between them.
The approach builds on 📝Model Routing Strategies but goes further. Routing chooses one model per request; orchestration combines several in a single job, for example one model planning, others executing in parallel, and a different model reviewing the result. Cross-model review can catch errors a model tends to miss in its own work.
The costs are integration and consistency. Each model has its own API, prompt conventions, and failure modes, and context must be translated as work moves between them. Teams often standardize on one hub agent that delegates to others through command-line tools or 📝MCP Server connections.
My terminal stack works this way: the Claude CLI is the hub, delegating to Codex and Kimi when a task suits them, all on subscriptions rather than metered API billing.