The GPT-5.6 picker gives me three model families and six effort levels. I use the task's ambiguity, review cost, and expected runtime to choose between them.
The Three Models
This is my operating heuristic, not an official capability ranking.
Sol handles my hardest work: architecture decisions, complex refactors, deep research, multi-step agent tasks, and final code review. I use it when the task is ambiguous or expensive to get wrong.
Terra is my daily driver when the plan is clear and the job needs reliable cross-file execution, analysis, automation, or technical writing.
Luna is useful for code reading, small fixes, summaries, translations, and batch classification. I reach for it when the task is narrow and easy to verify.
Pricing and quota behavior can change. Check the current product UI or official documentation before estimating cost.
Effort Levels
Each model offers Low, Medium, High, XHigh, Max, and Ultra.
A higher effort level gives the model more reasoning budget and usually takes longer. It does not guarantee a better answer. In my work, I increase effort only after the prompt and task boundary are clear.
My Default Setup
I use Sol High for most complex coding and multi-file work. For exploration and code reading, I drop to Luna. When a task keeps failing, I move to Sol Max after checking the context and prompt.
I start below Ultra. When Max fails, I inspect the prompt, context, and task split before adding more reasoning budget.
