aiqbench analysis
This technique implements a dynamic routing strategy where a cost-efficient base model delegates complex reasoning tasks to a frontier-tier advisor mid-generation. By restricting expensive inference to only the 10-20% of queries requiring high-level logic, this approach significantly lowers operational costs while maintaining solution quality. It represents a practical optimization for agentic workflows that balances performance ceilings with token expenditure.
Advisor: Give Any Model a Lifeline to a Smarter One
The openrouter:advisor server tool lets a fast, cheap model consult a stronger one mid-generation. Run GPT-4o Mini for the routine work. Call Claude Fable when it actually matters.
Add openrouter:advisor to your tools array and your model can ask a stronger model for help mid-generation. When the executor hits a hard decision, gets stuck, or wants a sanity check before finishing, it calls the advisor with a prompt. The advisor thinks, returns guidance as the tool result, and the executor keeps going with better information. Both roles are open: any model on OpenRouter can be the executor, and any model from any provider can be the advisor. Run a Gemini executor that consults Claude, or a GPT executor that consults DeepSeek. You pick the pairing. Try it in the chatroom or read the docs for…
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