GPT-6 arrives as two distinct models: Sol, the full-capability flagship, and Luna, a cost-optimized variant built for high-volume deployment. OpenAI positions both above GPT-4o on every major benchmark, with Sol claiming top scores on MMLU, HumanEval, and MATH as of release date.
The architecture split matters more than the naming. Luna is not a distilled afterthought. OpenAI engineered it separately to hit a specific cost-per-token target while preserving Sol's reasoning gains on tasks under a defined complexity threshold. The original piece details where that threshold breaks down, and the failure modes are specific and worth reading.
What comes next is the API pricing structure and the context window specs for each model, both of which determine whether this release actually changes how developers build. Those numbers are in the full post, and they reframe which model is the real story.
[READ ORIGINAL →]