GPT-6 Luna vs GPT-5.6 Luna

Generational comparisons usually involve a trade. This one mostly doesn't: GPT-6 Luna matches its predecessor wherever the table could make them differ in capacity, and comes out ahead on price and recency.

Identical where it matters for capacity

The same context window and maximum output, the same effort range from none to max with medium as the default, the same long-context rule, and the same rate-limit table — both sit in the high-throughput Luna group, with ceilings at the top usage tiers well above the Sol models'. A pipeline sized for GPT-5.6 Luna's throughput fits GPT-6 Luna unchanged.

Lower on every price cell

Input, cached input, cache writes and output are all cheaper on GPT-6 Luna, in both context bands and at every tier both publish, and the output gap is the widest. For the high-volume, output-heavy work Luna models usually carry — extraction, transformation, summaries — that's where the saving lands.

Newer training data

GPT-6 Luna's knowledge cutoff is several months later than GPT-5.6 Luna's, and the newest of any model on this site. For focused coding against recent libraries, that can matter more than price.

The only reasons to stay

Behavioral continuity on a workload you've already tuned, or a client and plan that haven't received GPT-6 Luna yet — availability in Codex depends on plan, client and rollout, and Enterprise and Edu administrators have to enable it. Otherwise this is the rare migration where the newer model is the default answer. Run your evaluation set once before switching a production pipeline, since a new generation can change output style even when the specs match.