Match the model to the work.
Answer seven plain questions. Get a workload-fit recommendation, a benchmark-informed value alternative, and a stronger option.
Cost changes the answer.
Workload fit comes first. The benchmark adds a second lens: some smaller-model, higher-effort combinations can deliver compelling intelligence per dollar.

Advanced choices beyond the selector
Light, Medium, High, and Extra High tune the selected model. Use the lowest effort that produces the result you need. The selector reserves Light for tightly bounded Luna work and Extra High for unusually difficult or final reviews.
Max and Ultra solve different problems. Max gives one model more time for the hardest single task. Ultra brings in subagents for work that divides into meaningful parallel lanes. Most tasks need neither.
Guidance and benchmark evidence
OpenAI guidance determines workload fit. The third-party benchmark informs value alternatives. Test important workflows with representative work.
- OpenAI Codex model guidance
- Choosing Sol, Terra, or Luna
- Artificial Analysis GPT-5.6 benchmarks
- OpenAI API pricing
Choose for task fit first, then compare the value alternative. Escalate when ambiguity or the cost of being wrong outweighs the extra usage. · A free tool from Majestic Labs.
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