OpenAI has expanded its GPT-6 model family with the launch of GPT-6 Sol and GPT-6 Luna, introducing two models designed to bring capabilities from its flagship GPT-6 Astra model to faster and lower-cost workloads.
The models were released on September 22 and are available through the OpenAI API. They are also rolling out across ChatGPT Work and Codex for eligible paid users. OpenAI positions Sol as a model for more complex workloads, including coding and agentic tasks, while Luna is designed for high-volume, cost-sensitive tasks such as summarisation, information extraction and quick queries.
A key part of the launch is pricing. OpenAI said API pricing for the new Sol and Luna models is 50% lower than the promotional pricing of their GPT-5.6 counterparts, attributing the reduction to improvements in caching and inference efficiency.
For standard API processing with shorter prompts, GPT-6 Sol is priced at $2 per million input tokens, $0.20 per million cached input tokens and $10 per million output tokens. GPT-6 Luna costs $0.10 per million input tokens, $0.01 for cached input and $0.50 per million output tokens.
OpenAI is also claiming improvements in factual reliability and coding performance. According to the company, GPT-6 Sol produced about half as many mistakes as its predecessor in an internal factuality evaluation based on de-identified conversations where users had flagged model errors. As the results come from OpenAI's own evaluation, they should be treated as company-reported performance rather than independent benchmarking.
Both models accept text and image inputs and generate text outputs. OpenAI's model documentation lists context windows of up to 1.05 million tokens for Sol and Luna, along with support for tools including web search, file search, functions and computer use.
The models extend a GPT-6 lineup that began with GPT-6 Astra earlier in September. Astra remains positioned as OpenAI's flagship model for its most demanding reasoning and coding workloads, while Sol targets a balance between capability and cost and Luna focuses on efficiency at scale.
The launch also comes amid continued competition among AI developers to improve model performance while reducing the cost of deploying generative AI in enterprise applications. For businesses, lower inference costs could affect how extensively AI is deployed across coding, automation, document processing and agent-based workflows.
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