OpenAI says new GPT-6 models cost half as much
Paid Work and Codex users can try both; free users get Luna in the desktop app, and basic Chat still has neither.
By Saeed Ezzati7 min read
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OpenAI’s new GPT-6 Sol and Luna models come with a clear price promise—and a less straightforward access story. The company says both cost 50 percent less than their corresponding GPT-5.6 models, and positions them as less-intensive alternatives to GPT-6 Astra, for work at different scales. On the API rate card, Sol costs $2 per million input tokens and $10 per million output tokens. Luna is much cheaper: 10 cents for input and 50 cents for output, per million tokens. OpenAI says the pair use training methods similar to Astra’s and make half as many factual errors as their predecessors. It also reports that Luna scored 5.4 percent higher than the GPT-5.6 release on AutomationBench, while costing 58 percent less per task. Those are company-reported results, not a promise of the same savings or performance in a particular deployment. The practical test is whether a model handles a team’s own work well enough to make the lower rate matter. Access is also tied to the product and subscription. Since September 22, both models have been available in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu customers. Go and Free users can access Luna in the ChatGPT desktop app, but neither model had reached basic Chat at publication. So the first questions for teams are simple: does their route include the model, and how does it perform on their tasks? Lower prices matter only when access and results line up. That gap between a product’s promise and its implementation is also at the heart of Meta’s Muse debate. Meta says the personal agent was built from scratch, while acknowledging that OpenClaw heavily inspired it. Users pointed to matching workspace filenames and nearly identical content in a file called SOUL.md; Meta did not dispute those similarities. Product chief Nat Friedman said the team considered OpenClaw’s conventions well judged. Meta says Muse runs in a dedicated virtual machine, with a separate Sentinel agent checking whether activity can reach the internet and seeking permission when needed. Users can also choose connected apps and change access. Those are Meta’s stated safeguards, central to its argument that Muse is designed for broad consumer use—not a resolution of the questions about how closely its design follows OpenClaw. And in factories, the question shifts from software design to how people and machines share the work. Toyota aims to deploy 400,000 robots: 150,000 at its automotive plants and 250,000 at group-company facilities. That is a broad factory-robotics target, not a count of humanoids. Toyota has not said how many will be humanoid, or how many robots it already has in operation. Workers are teaching ELEY, a wheeled, two-handed humanoid, precise assembly movements. Toyota says people and robots should coexist. Nikkei Asia reported that the group plans to spend $6.42 billion annually on factory-robot upgrades beginning in 2028; the company has not detailed how the planned machines will divide tasks with workers. The same deployment challenge is larger still in Google and the Gates Foundation’s plan for farmers. They have raised their intended reach from 50 million to 200 million smallholder farmers across Sub-Saharan Africa and South Asia. The partners say more than $100 million has been directed to the work, which includes farm forecasting, crop research, and speech and text datasets in more than 40 African languages. Regional organizations are meant to help build and deliver the tools. But a multi-year funding commitment and a reach target are not evidence of improved farm decisions or yields. Across these stories, the useful thing to watch is what happens after a system is made available: who can actually use it, whether it fits local work, and whether results hold up beyond the announcement.
