Sending work to GPT-6 Luna saves more than OpenAI's halving of Sol's price

Both of Sol's rates fell by exactly half ^[4], so the per-token saving holds for any mix of input and output. Teams budget per task, though, and every cost figure OpenAI published is a per-task number taken at xhigh or max reasoning effort ^[8][9]. If GPT-6 Sol spends the same tokens on the AutomationBench task that GPT-5.6 Sol would have, that $0.27 task would have cost $0.54 at the old rates ^[4]. If it spends twice as many, the bill does not move ^[6]. The reports do not include token counts per task, and Gizmodo reported that customers have been complaining about token costs that are steep and unpredictable ^[16].

The halving is measured against GPT-5.6's promotional prices ^[3]. A budget written on those rates was already written on a discount. OpenAI's guidance that the new models are drop-in upgrades ^[17] invites teams to swap the model name without re-measuring what a task consumes.

Luna's evidence is thinner than Sol's. Every benchmark score in the two reports belongs to Sol ^[8][9]. For Luna, Crowdfund Insider reports only that OpenAI presents it as delivering useful performance at a much lower price than earlier mid-tier models ^[7], and the company aims it at summarization, information extraction and short-answer questions ^[6]. A team moving those jobs down a tier is relying on its own tests of Luna's accuracy on its own documents.

Caching changes the tier comparison for agent work that re-sends a long prompt each turn. Reasoning effort and tool availability can now change mid-conversation without breaking the cache ^[13], so an agent can raise effort for one hard step and keep its cached prefix. GitHub reports that over recent months OpenAI's caching changes cut the share of prompt tokens needing fresh processing by more than 50% across billions of requests ^[14].

The competitor comparisons need a discount of their own. OpenAI took rival scores from public reports and used Fable 5 figures where Fable 5.1 numbers were unavailable ^[10]. The Opus 5 results Sol beat belong to a model Anthropic followed with Opus 5.5 on the same day ^[15].

In my view the budget line that moves most is the share of work a team can push to Luna, with the Sol halving second ^[2]. That view fails if little of a team's work fits Luna's brief of well-defined, high-volume jobs ^[6], or if Luna misses often enough on extraction and summarization that retries and escalations to Sol consume the tier discount ^[2].