Claude Orchestrates GPT and Gemini - Multi-Model Routing for Desktop Automation
Claude Orchestrates GPT and Gemini - Multi-Model Routing for Desktop Automation
Not every task in a desktop automation workflow requires the most capable model. When an agent reads a screenshot to check if a dialog box appeared, that does not need Claude Opus. When it generates a complex multi-step plan for a workflow that touches five applications, that probably does.
Multi-model orchestration means using the right model for each subtask. Claude handles planning, reasoning, and complex decisions. Cheaper, faster models handle routine execution steps.
How Routing Works in Practice
The orchestrator model - typically Claude - breaks a high-level task into steps. For each step, it decides which model should execute it based on the complexity.
"Open Safari, navigate to this URL, fill in three form fields, click Submit" is a sequence of simple actions. Each one can be handled by a smaller model that is faster and cheaper. The orchestrator only needs to step in when something unexpected happens - an error dialog, a CAPTCHA, a page that loaded differently than expected.
The Cost Difference is Significant
For a typical desktop automation session, 80% of the tokens go to routine operations - reading element labels, confirming button positions, executing clicks. These tokens cost the same per-token as the 20% that goes to actual reasoning. By routing the routine 80% to a model that costs a fraction of the price, you can cut total costs dramatically without any reduction in the quality of the planning and decision-making.
Implementation Patterns
The simplest approach is task-type routing. Vision tasks - "what is on screen right now" - go to a fast vision model. Planning tasks - "given this error, what should I do next" - go to Claude. Execution tasks - "click the button labeled Submit" - go to the cheapest model that can reliably format a tool call.
A more sophisticated approach uses confidence-based routing. Start with a cheap model. If it returns a low-confidence answer or an unexpected result, escalate to a more capable model. This handles the common case cheaply while still getting the best model for edge cases.
The key insight is that model selection should be dynamic, not static. Different parts of the same workflow have different complexity requirements, and a good orchestrator adapts accordingly.
Fazm is an open source macOS AI agent. Open source on GitHub.