Match Reasoning Effort to Task Length in Codex (Astra/Sol)

Melvin Vivas · X post · 2026-09-11 · Open on X

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

Summary

A short tip on picking reasoning levels for coding-agent models. Low reasoning on Astra doesn't hold up for long, sustained work, so medium is a sensible minimum. High and xhigh work well but cost more. You can also have one model plan for another.

Key points

• Astra on low reasoning isn't suited to long, sustained tasks.
• Use medium reasoning as a practical minimum for real work.
• High/xhigh gives better results but costs more.
• Scale reasoning effort to the task's length and complexity.
• Pattern: let Sol orchestrate and use Astra as a planner agent.

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
• GPT 5.6 Luna · tool · openai.com · paid
  The OpenAI model used inside Codex for the demo. The transcript gives the variant name as 'Soul', which is unclear.
  Also in: Set Codex subagent model and reasoning to save usage limits (Melvin Vivas on X · notes), Use GPT-5.6 Luna in Codex for Terminal Tasks (Melvin Vivas on X · notes), Run Coworker desktop agents cheaply with GPT-5.6 Luna on OpenRouter (Melvin Vivas on X · notes), Codex Config: GPT-6 Astra Orchestrator + GPT-5.6 Luna Subagents at Medium (Melvin Vivas on X · notes) and 24 more

Try this

• Use at least medium reasoning for long tasks; save high/xhigh for complex work.