This is not an official configuration, but a personal routing setup shared by a Codex user: use Luna for routine implementation, brainstorming, and subagents; use Sol high for plan reviews and final decisions; and use a fast model for test execution. It turns “use a cheaper model for high-frequency work and a stronger model as the quality gate” into an executable configuration.
use_subagent = true
subagent_model = "gpt-5.6-luna-xhigh"
review_model = "gpt-5.6-sol-high"
best_model = "gpt-5.6-sol-high"
normal_model = "gpt-5.6-luna-max"
fast_model = "gpt-5.3-codex-spark-xhigh"I use a fork of superpower (a much lighter fork), and I keep the plan review in sol-high, planning / brainstorming in lu… This is a necessary excerpt; read the original source for full context.
Test it first in a small, reversible repository; do not treat a personal fork or configuration as an official Codex capability.
Set an explicit review checklist and stopping conditions for review_model to prevent the review agent from looping indefinitely.
Before parallelizing subagents, confirm that the tasks are independent of one another, and limit concurrency, budget, and writable directories.
Treat best_model as the final verification role, rather than the default model for every step.
The original comes from the comments section of a Reddit post, and the author also mentioned using a custom lightweight fork. The configuration may depend on fields and implementation in that fork and may not work with native Codex. The TOML and notes above are preserved from the text visible on the page and were not rewritten as official documentation.
GPT-5.6 Luna