OpenAI introduced GPT-6 Sol and Luna to extend GPT-6 Astra to cheaper, faster tiers and cut API prices 50% versus GPT-5.6 promotional rates. Sol falls to $2 input and $10 output per million tokens, Luna to $0.10 input and $0.50 output.
That pricing only works after Astra.
Earlier this month OpenAI introduced GPT-6 Astra as its most intelligent and aligned model, then said work happens at different scales, rhythms and budgets. Sol and Luna were trained with similar methods as Astra and inherit its gains in professional work, factuality, coding, computer use and alignment. OpenAI says improvements in caching and inference let it serve the models more efficiently and pass savings to users, with 90% discounts on cached input-token reads still in place.
The price ladder now has a clear anchor. Astra remains the top tier at $10 per million input tokens and $50 per million output tokens with a 1,050,000 token context window and up to 128,000 tokens of output. Sol sits below Astra on cost and above Luna on capability, Luna at the bottom for high-volume tasks. For buyers asking is GPT-6 API pricing real and is it legit, the numbers are real and published for API use, is it safe to adopt depends on workload fit rather than model risk.
OpenAI frames the value as cost per task, not just tokens. On AutomationBench 1.0.6 across 47 tools, GPT-6 Sol at xhigh effort scores 33.2% at $0.27 per task, ahead of Claude Opus 5 at max effort at 26.9% and about 11.1 times its cost. On Agents’ Last Exam V1, Sol at max scores 56.4%, above Opus 5’s best at 60% lower cost. On FrontierCode 1.1, which grades mergeability as well as correctness, Sol matches Claude Fable 5.1 at xhigh at much lower cost. On DeepSWE 1.1, Sol at max hits 68.8%, within 1.1 points of Fable 5 at xhigh at about 80% lower cost, while Luna at max hits 66.6% at 93% less than Opus 5 at medium. On OSWorld 2.0 offline, Sol at xhigh reaches 60.5%, similar to Opus 5 at medium at about 80% lower cost.
