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OpenAI Unveils GPT-6 Sol and Luna Models, Halving API Costs While Rivaling Top Flagship Performance
OpenAI has officially unveiled the newest additions to the GPT-6 lineup: GPT-6Sol and GPT-6Luna. Built using a training approach inspired by GPT-6Astra, these models aim to deliver Astra’s top-tier performance in professional tasks, factual accuracy, coding, computer use, and alignment within faster, more affordable small-scale architectures. While GPT-6Astra remains the go-to choice for those seeking uncompromised quality, Sol and Luna demonstrate exceptional competitiveness at a significantly lower cost.
On the pricing front, API rates for GPT-6Sol and Luna are slashed by 50% compared to GPT-5.6’s promotional pricing. Additionally, OpenAI has enhanced prompt caching for developers leveraging GPT-6, enabling a higher default cache hit rate. This optimization allows for smarter context reuse, resulting in quicker response times and further cost savings.
Key performance highlights include:
Business workflows and agent capabilities: In the AutomationBench cross-application process tests, GPT-6Sol outperformed Claude Opus5 under high-intensity conditions, costing just 9% per task. In the Last Exam agent evaluation, GPT-6Sol surpassed Claude Opus5’s highest score while reducing task costs by 60%.
Factual reliability: In de-identified real-world conversations, GPT-6Sol’s error rate is roughly half that of its predecessor, matching Astra’s reliability standards. Luna has also seen substantial improvements in factual accuracy.
Programming and software engineering: Both models are heavily optimized for AI Coding Agent workloads. In the DeepSWE v1.1 software engineering benchmark, GPT-6Sol scored 68.8%, falling just 1.1 percentage points short of the top score, with single-task costs reduced by approximately 80%. Luna’s performance rivals mid-tier competitors, offering a distinct cost advantage.
Computer operation capabilities: In the OSWorld2.0 offline tests, GPT-6Sol achieved results comparable to mid-tier competitors, with single-task costs roughly 80% lower. Meanwhile, Luna surpassed the previous generation’s flagship model, operating at just one-tenth of its cost.
Beyond core performance gains, OpenAI has integrated the refined communication style from GPT-6Astra into Sol and Luna. This makes the new models clearer and more concise in technical and coding discussions, significantly cutting down on jargon and low-value details. Regarding value alignment, both models show improvements over GPT-5.6, including a marked reduction in misleading statements during coding tasks.
GPT-6Sol and GPT-6Luna are now fully accessible in ChatGPT Work and Codex for all Plus, Pro, Business, Enterprise, and Edu users. Free and Go users can also access GPT-6Luna via the desktop application. In the OpenAI API, these models are available under the identifiers gpt-6-sol and gpt-6-luna.
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OpenAI has officially unveiled the newest additions to the GPT-6 lineup: GPT-6Sol and GPT-6Luna. Built using a training approach inspired by GPT-6Astra, these models aim to deliver Astra’s top-tier performance in professional tasks, factual accuracy, coding, computer use, and alignment within faster, more affordable small-scale architectures. While GPT-6Astra remains the go-to choice for those seeking uncompromised quality, Sol and Luna demonstrate exceptional competitiveness at a significantly lower cost.
On the pricing front, API rates for GPT-6Sol and Luna are slashed by 50% compared to GPT-5.6’s promotional pricing. Additionally, OpenAI has enhanced prompt caching for developers leveraging GPT-6, enabling a higher default cache hit rate. This optimization allows for smarter context reuse, resulting in quicker response times and further cost savings.
Key performance highlights include:
Business workflows and agent capabilities: In the AutomationBench cross-application process tests, GPT-6Sol outperformed Claude Opus5 under high-intensity conditions, costing just 9% per task. In the Last Exam agent evaluation, GPT-6Sol surpassed Claude Opus5’s highest score while reducing task costs by 60%.
Factual reliability: In de-identified real-world conversations, GPT-6Sol’s error rate is roughly half that of its predecessor, matching Astra’s reliability standards. Luna has also seen substantial improvements in factual accuracy.
Programming and software engineering: Both models are heavily optimized for AI Coding Agent workloads. In the DeepSWE v1.1 software engineering benchmark, GPT-6Sol scored 68.8%, falling just 1.1 percentage points short of the top score, with single-task costs reduced by approximately 80%. Luna’s performance rivals mid-tier competitors, offering a distinct cost advantage.
Computer operation capabilities: In the OSWorld2.0 offline tests, GPT-6Sol achieved results comparable to mid-tier competitors, with single-task costs roughly 80% lower. Meanwhile, Luna surpassed the previous generation’s flagship model, operating at just one-tenth of its cost.
Beyond core performance gains, OpenAI has integrated the refined communication style from GPT-6Astra into Sol and Luna. This makes the new models clearer and more concise in technical and coding discussions, significantly cutting down on jargon and low-value details. Regarding value alignment, both models show improvements over GPT-5.6, including a marked reduction in misleading statements during coding tasks.
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