For most people using AI in marketing today, this changes nothing. If your team uses ChatGPT, Jasper, Copy.ai, or any consumer-facing AI writing tool, those products have their own pricing. Your subscription did not get cheaper overnight. API pricing is what developers and technical teams pay to build directly on the model, it does not flow through to consumer products automatically.

But for teams building custom AI workflows, or thinking about it, an 80% price drop on the budget tier is worth actually doing the math on.

What the numbers mean in practice

At the old Luna pricing ($1 input, $6 output per million tokens), running 10,000 short social media posts through the API, roughly 100 tokens in, 150 tokens out per post, would cost about $1 in input and $9 in output. Around $10 total.

At the new pricing, the same run costs roughly $0.20 in input and $1.80 in output. Under $2 total.

That is not a scenario where a big team saves money on one workflow. That is a scenario where a small business or solo founder can run an automated content pipeline, email personalisation system, or product description generator at a cost that is essentially negligible.

The workflows that were economically borderline before, where API cost was close to the value generated, are now clearly viable. The ones that were already profitable just got significantly more so.

What is still expensive

Sol, OpenAI’s best-performing model, did not move. It is still $5 per million input tokens and $30 per million output tokens. For tasks that genuinely require Sol’s reasoning capability, you are paying the same as before.

This matters because the price cuts apply to models that handle simpler, higher-volume tasks well, not the one you would use for complex strategic analysis or nuanced long-form writing. Test the task on Luna first, but do not assume the cheapest model will do every job. The cost difference between Luna and Sol is significant enough that getting the choice wrong at scale adds up fast.

The other thing worth noting: OpenAI’s engineering team cited a 20% reduction in serving costs thanks to infrastructure improvements, including using Sol to rewrite their own GPU kernels. The Luna cut goes far beyond a cost pass-through, it is a strategic price decision to keep developers on OpenAI’s platform as Chinese providers and Google compete aggressively on price.

Which marketing workflows benefit most

The tasks where Luna-level models typically perform well enough include: generating product descriptions at scale, first-draft social captions, bulk email subject line testing, FAQ generation from existing documentation, and translation of standard marketing copy.

The tasks where you generally still need a better model: anything requiring nuanced brand voice, complex reasoning about strategy, sensitive communications, and long-form content where quality needs to match strong human writing.

A useful approach for teams evaluating this: pick one high-volume task you currently do manually or with an expensive tool, run 50 examples through Luna at the new pricing, and score the output quality against your standard. The economics make testing easy now, you can run a meaningful sample for less than a dollar.

The broader picture

API pricing has been falling consistently across providers. A year ago, the compute cost for processing a million tokens was several times what it is today across all models. The ceiling on what is economically viable to automate keeps rising.

The question for marketing teams is not really about price anymore. It is about which tasks are actually worth automating, which require human judgment that AI reliably gets wrong, and how to build a workflow that handles both. The decision to build on the API instead of using off-the-shelf tools is still primarily about control and capability, price is now rarely the limiting factor.

If you want to understand which AI marketing workflows are worth building custom versus buying as a tool, that is the kind of practical systems question mark8ng.ai is built to help with.

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