OpenAI has expanded its GPT-6 model family with GPT-6 Sol and GPT-6 Luna, two new models designed to make advanced AI more affordable to use at a large scale. The models were released on September 22, shortly after OpenAI introduced GPT-6 Astra, its main model for complex reasoning and coding.
OpenAI says Sol and Luna improve factual accuracy, coding, computer use, professional tasks and AI agents. The company also says the models cost much less to run than earlier systems. OpenAI credits the lower cost to improvements in caching and the way the models process information.
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GPT-6 Sol and Luna Are Built for Different AI Tasks
OpenAI is targeting GPT-6 Sol and GPT-6 Luna at different types of AI tasks.
GPT-6 Sol is built for more demanding work, especially complex coding and AI agent tasks. OpenAI says Sol offers a balance between performance and cost. This makes it useful for developers who need strong reasoning but do not want to use the company’s most expensive model.
GPT-6 Luna is designed for focused tasks that need to be handled at high volume. It puts more emphasis on keeping costs low and is described by OpenAI as its most efficient model for these workloads.
Both models have a 1.05 million-token context window and can generate up to 128,000 output tokens. They support text and image inputs and are available through OpenAI’s Responses and Chat Completions APIs.
OpenAI Lowers API Prices With GPT-6 Sol and Luna
OpenAI has also focused on making its new GPT-6 models cheaper to use. The company says GPT-6 Sol and GPT-6 Luna are 50% cheaper than the promotional prices of their GPT-5.6 counterparts. The standard API pricing for the two models is:
| Model | Input per 1M tokens | Cached input | Output per 1M tokens |
| GPT-5.6 Sol | $4 | – | $20 |
| GPT-6 Sol | $2 | $0.20 | $10 |
| GPT-5.6 Luna | $0.20 | – | $1.20 |
| GPT-6 Luna | $0.10 | $0.01 | $0.50 |
These prices apply to standard API use for prompts with up to 272,000 input tokens. OpenAI also has different pricing options for longer prompts, batch processing and other types of API use.
OpenAI says improvements to its caching system have helped reduce costs. The company has increased the default cache-hit rate and offers discounts when eligible shared parts of a prompt are reused within a 30-minute period.
GPT-6 Sol Delivers Fewer Errors in OpenAI Tests
OpenAI says the new models also improve factual accuracy. The company tested GPT-6 Sol using de-identified real-world conversations where users had previously reported errors. In this test, OpenAI says GPT-6 Sol made about half as many mistakes as its predecessor.
Its performance also came close to the more expensive GPT-6 Astra. GPT-6 Luna also performed better than GPT-5.6 Luna when tested with higher reasoning settings. However, these results come from OpenAI’s own evaluation. They show the company’s reported performance and have not been independently verified.
Sol Targets Coding and AI Agents
GPT-6 Sol is created for developers building coding agents and AI systems that can handle multi-step tasks.
OpenAI says Sol performs much better than GPT-5.6 Sol on FrontierCode, a benchmark that tests whether AI coding agents can make changes that are ready to be added to real software projects. The test looks at more than whether the code works. It also checks testing, coding style, the size of the changes and whether the code follows the existing project’s standards.
OpenAI also reports improvements on AutomationBench, which tests AI agents on complete business tasks using 47 tools across areas such as sales, marketing, operations, customer support, finance and HR.
At its highest reasoning setting, OpenAI says GPT-6 Sol performed better than Claude Opus 5 on the benchmark while costing much less per task. GPT-6 Luna also performed better than its previous version while reducing the cost of completing the benchmark tasks.
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GPT-6 Models Improve Computer-Use Performance
The new GPT-6 models also show improvements in tasks that require AI to interact with computer interfaces. On the OSWorld 2.0 offline benchmark, OpenAI says GPT-6 Sol scored 60.5% at its highest reasoning setting.
Claude Opus 5 scored 60.3% at medium effort in the same comparison. OpenAI says Sol achieved its result at about 80% lower cost per task. GPT-6 Luna also performed better than GPT-5.6 Sol at a lower reasoning setting, while costing about one-tenth as much in OpenAI’s comparison.
OpenAI continues to position GPT-6 Astra as its strongest model for computer-use tasks, while Sol and Luna are aimed at users who want lower-cost options.
GPT-6 Models Aim for Clearer and More Precise Responses
OpenAI says GPT-6 Sol and Luna also improve the way they communicate with users. The company says the models give shorter and more focused responses, with less jargon, fewer unnecessary details and fewer unusual phrases. The aim is to keep the important information while making technical and coding discussions easier to follow.
OpenAI also reports improvements in its alignment tests, including evaluations that check how models respond to misleading claims about work they have completed. However, the company says these tests are designed to create challenging situations and should not be treated as estimates of how often these problems occur in normal use.
GPT-6 Sol and Luna Give Developers More Model Choices
The release gives developers more options when choosing an OpenAI model for different types of work. OpenAI positions GPT-6 Astra for the most demanding reasoning and coding tasks, GPT-6 Sol for workloads that need a balance between performance and cost, and GPT-6 Luna for high-volume tasks where keeping costs low is a priority.
The lower token prices could help businesses reduce the cost of running AI agents, coding tools and large-scale text-processing systems. This could be particularly useful for AI agents, which may need multiple model calls to complete a single task.
The launch also shows OpenAI’s focus on making AI more affordable to run at scale. By offering models at different performance and price levels, OpenAI gives developers more flexibility to choose a model based on the complexity and volume of their workloads.