Iteratively select LLM models for agent tasks
Prototype with the most capable model, then swap in smaller models to optimize for cost and latency.
Build your agent prototype with the most capable model for every task to establish a performance baseline, then try swapping in smaller models.
Different models have tradeoffs in complexity, latency, and cost. Start with the best to ensure functionality, then optimize by using smaller, faster models for simpler tasks where accuracy targets are still met.
Use a powerful model for refund approval decisions, but a smaller model for simple data retrieval or intent classification within the same workflow.
When building AI agents, start by using the most powerful LLM for all tasks to set a performance benchmark. Then, test smaller, faster, and cheaper models for specific tasks to find the right balance of capability and efficiency.
Do not prematurely limit an agent's abilities by choosing a smaller model from the start.
- A Practical Guide to Building AI Agents ↗OpenAI · OpenAI News · Jan 14, 2026 · paraphrased
First seen on Subvolts Jan 14, 2026. Updated Oct 9, 2026. Spotted a problem? Tell us.
- A Practical Guide to Building AI AgentsOpenAI · OpenAI News · Jan 14, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- OpenAI (OpenAI News). Subvolts collected, normalized and credited it; the link above opens the original.