Choosing the right GPT‑5.6 configuration can feel overwhelming when you need to balance performance, latency, and budget. The three capability tiers—Sol, Terra, and Luna—are designed to progress on independent release cadences, so picking the one that matches your workload’s complexity is the first step to avoid over‑paying for unnecessary power.
Start by estimating your per‑request token volume. Enter the expected input and output tokens into the cost calculator; the tool applies OpenAI’s published per‑1M rates and automatically factors in the 90% discount for cached reads. Remember that cache writes are billed at 1.25× the uncached input rate and require a minimum 30‑minute life, so plan your caching strategy around repeated prompts or reusable context to maximize savings.
Next, compare benchmark scores side‑by‑side. The explorer shows Terminal‑Bench 2.1, BrowseComp, and SEC‑Bench Pro results for each tier, letting you see where a higher‑priced model actually gains accuracy or speed. If a dash appears, that metric wasn’t reported—treat it as unknown rather than assuming zero performance.
For workloads that demand stronger reasoning or faster turnaround, enable Ultra mode. Ultra runs four agents in parallel by default, boosting scores on the three benchmarks while increasing token consumption. Toggle the Ultra button to see the trade‑off against Sol’s single‑agent baseline, and consider whether the extra cost aligns with your latency or quality targets.
Finally, verify where you can run each setup: Ultra is available in ChatGPT Work for Pro/Enterprise and in Codex for Plus tiers; API users can replicate similar flows via the multi‑agent beta in the Responses API. By systematically adjusting tier, token volume, caching, and agent count, you can pinpoint the most cost‑effective configuration that meets your performance goals.
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