Kenhuang Academy
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      • Codex Model Selection

      Choosing the right model and reasoning effort helps balance quality, speed, and cost.

      Available Models (2026)

      Model availability depends on your account and product surface. The tutorial uses names such as GPT-5.5, GPT-5.4, GPT-5.4-mini, and Codex Spark as examples; always check the current official model list.

      Reasoning Effort

      Reasoning effort controls how much thinking budget Codex spends.

      reasoning_effort = "medium"

      Higher effort is useful for architecture, debugging, refactors, and ambiguous product work. Lower effort is better for routine edits.

      How To Choose By Task

      TaskSuggested Choice
      Small copy or style editFast model, low effort
      Normal coding taskBalanced model, medium effort
      Deep debuggingStronger model, high effort
      Large design or migrationStronger model, high or extra-high effort

      Switching In Each Surface

      The Desktop App, CLI, IDE extension, and Cloud may expose model switching differently, but the concept is the same.

      Cost And Rate Limits

      Higher-end models and higher reasoning effort can cost more and may hit limits sooner. Use the smallest setup that gets reliable results.

      Next Steps

      Continue with Subagents.

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