Cut agent costs by giving chore subagents a cheaper model

Melvin Vivas · X post · 2026-09-15 · Open on X

Topics: AI Agents, Tool Use & MCP, LLMOps, Deployment & Monitoring, AI Dev Tools & Productivity · Level: intermediate

Summary

The creator says routine chore tasks don't need a top model like Astra. His subagent runs on Luna at medium reasoning effort, which saves money. The lesson is to match each subagent's model to how hard its task is.

Key points

• Chore tasks (routine, low-complexity work) don't need the strongest, most expensive model.
• Give subagents a cheaper, smaller model (here Luna at medium effort) and save the top model (Astra) for hard reasoning.
• Choosing a model per task is a simple way to lower the cost of agent workflows.

Resources mentioned

• GPT-6 Astra · tool · openai.com · paid
  The model announced in the quoted launch post, pitched as the developer's most capable model for work, coding, science and cybersecurity, and able to operate a computer.
  Also in: Customizing Your Coding Setup with Pi Coding Agent Extensions (Melvin Vivas on X · notes), Pi Agent Council: Ask Multiple LLMs in Parallel and Compare Their Advice (Melvin Vivas on X · notes), Use GPT-6.1 Sol by Default, Save Astra for Emergencies (Melvin Vivas on X · notes), Dots in ChatGPT: always-on AI agents that you hand responsibilities to (Melvin Vivas on X · notes) and 49 more
• Luna · tool · openai.com · paid
  The other model/agent the classifier routes tasks to; the post doesn't describe it further.
  Also in: GPT-6.1 Sol May Beat Luna for Subagents (Melvin Vivas on X · notes), Picking models for orchestrator and subagent roles in Codex (Melvin Vivas on X · notes), GPT-6 Sol Ultra Subagents Use Up Limits Fast (Melvin Vivas on X · notes), GPT-6 Sol vs Opus 5.5 in a Livestream Comparison (Melvin Vivas on X · notes) and 14 more

Try this

• Give subagents that handle routine chores a cheaper model or lower reasoning effort to save cost.