Dynamic Model Routing: Use Expensive AI for Planning, Cheaper Models for Execution
A practical workflow to cut AI costs by assigning high-performance models to planning and cheaper alternatives to task execution.
Practical Summary
This entry explains a user-tested strategy for reducing AI tool costs by strategically routing tasks between models based on their complexity and cost.
Why It Matters
As AI API costs vary widely, this workflow helps teams and developers maintain high-quality outputs on complex tasks while significantly reducing overall spend by using cheaper models for well-defined execution steps.
The Core Workflow: Separate Planning from Execution
The strategy involves using a high-performance (and often more expensive) AI model specifically for the planning and problem-definition phase of your work. This model's superior reasoning can tackle complex, 'ugly edge cases' and define the task clearly.
Once the work is well-defined by the planning model, you switch to a less expensive model to actually execute the defined tasks. This separates the high-cost reasoning from the more straightforward execution work.
Step-by-Step Implementation
1. Identify a complex task or problem with potential edge cases. Initiate a dedicated chat session with your high-performance planning model (e.g., a model like 'Claude Fable 5' as referenced in the source).
2. Use this model in 'plan mode' to analyze the task, define requirements, and resolve any complex issues. The goal is to produce a clear, well-defined work plan.
3. Once the task is fully defined and broken down into clear steps, open a new session or switch to a cheaper, high-throughput model (e.g., models like 'GPT-5.5-high/med' or 'Composer 2.5' as mentioned in the source).
4. Use this execution-focused model to carry out the predefined steps. This leverages the planning model's output to minimize the need for expensive reasoning during execution.
Key Consideration: Dynamic Routing is Now Essential
With AI token prices diverging significantly between models and providers, dynamically routing tasks based on complexity and phase (planning vs. execution) is moving from a cost-saving 'hack' to a standard best practice for managing AI tool budgets.