OpenAI announced on Tuesday, the 22nd, the expansion of its new line of artificial intelligence (AI) models with the launch of GPT-6 Sol and GPT-6 Luna. These models follow GPT-6 Astra, which the company presented at the beginning of September, and were created to provide some of the advancements of the more robust version, but with lower costs.
According to OpenAI, both Sol and Luna were trained using methodologies similar to those employed in Astra, integrating progress in various areas such as professional activities, software development, computer interaction, factual accuracy, and alignment.
The fundamental distinction lies in the ratio between capability and price. The company reported that optimizations made in caching and inference enabled a 50% reduction in API access values compared to the promotional prices of equivalent GPT-5.6 models.
Performance in Professional and Programming Tasks
GPT-6 Sol received special focus for professional and programming applications. OpenAI claims that this model shows notable improvements over GPT-5.6 Sol in tests focused on agents capable of operating in real code repositories.
In the FrontierCode 1.1 Main benchmark, which evaluates not only code functionality but also criteria such as test quality, organization, and adherence to project standards, the company reported a significant evolution of Sol compared to its predecessor.
In another test, DeepSWE v1.1, dedicated to complex software engineering challenges in practical projects, GPT-6 Sol achieved 68.8% with maximum effort. OpenAI highlighted that this result was only 1.1 percentage points away from the maximum score of Claude Fable 5, which was 69.9%, but with a cost per task approximately 80% lower.
GPT-6 Luna also achieved a performance of 66.6% in this same test, as disclosed by the company. Such functionalities were also implemented in Codex, OpenAI's platform focused on coding tasks, allowing developers to perform more coding tasks and use AI agents due to reduced costs.
OpenAI also highlighted the results of GPT-6 Sol in professional tasks involving the use of multiple tools and applications. In AutomationBench, which evaluates agents in workflows covering sales, marketing, operations, support, finance, and human resources, Sol reached 33.2% with maximum effort. This index surpassed Claude Opus 5, which recorded 26.9% in the test, according to the company, and the estimated cost per task was also considerably lower for Sol.
Additionally, in the test named Agents’ Last Exam, GPT-6 Sol achieved 56.4% with maximum effort. OpenAI stated that this result exceeded the highest score recorded by Claude Opus 5 in the evaluation, which was 60%, but with a 60% lower cost per operation.
Computer Usage
Computer handling is another area where OpenAI highlighted advances. In the OSWorld 2.0 offline test, GPT-6 Sol, using additional effort, achieved 60.5%, contrasting with the 60.3% of Claude Opus 5 under average effort. OpenAI declared that this achievement was reached with a cost per task about 80% lower.
Meanwhile, GPT-6 Luna demonstrated surpassing GPT-5.6 Sol in average effort in the same type of evaluation, costing approximately one-tenth of the value per task.
Despite the improvements presented, OpenAI maintained GPT-6 Astra as its reference model for tasks related to computer usage.
In addition to technical gains, Sol and Luna incorporated changes in the communication style established in GPT-6 Astra. OpenAI predicts that the new models will provide clearer answers, with a decrease in jargon, unusual constructions, and low-value details. The expectation also includes slightly more concise answers, without compromising content.
This change should be particularly noticeable in technical dialogues and those related to programming. The launch of Sol and Luna accelerates the expansion of the GPT-6 family. The first model of this generation, GPT-6 Astra, was launched at the beginning of September, focusing on functions such as programming, scientific research, computer usage, cybersecurity, and professional work.
With these models, OpenAI now offers options with distinct combinations of capability and cost. While Astra remains at the cutting edge, Sol and Luna aim to democratize part of the new generation's resources for tasks requiring higher processing volume.
The introduction of these two models also strengthens the company's strategy to reduce operational costs for AI agents and applications, especially in scenarios where systems need to execute numerous sequential operations. Sol and Luna are already available through OpenAI in its products and API, expanding access to the new generation of models.