GPT-6 Sol comes to Caffeine for faster app building
Caffeine has added OpenAI’s GPT-6 Sol to its lineup of AI models for app development, giving users another option for longer development sessions where features are added, tested and refined over multiple rounds.
The new model is positioned as a faster and lower-cost alternative to GPT-6 Astra, OpenAI’s flagship model. OpenAI describes GPT-6 Sol as a model designed to balance intelligence and cost, with support for complex coding and agentic workflows.
For Caffeine users, the addition is particularly relevant to the way applications are built on the platform. Caffeine allows users to describe what they want to create in natural language, with the platform handling application development across the frontend, backend and data storage. Applications built through Caffeine run on the Internet Computer.
The choice of model can matter during an extended build. A developer may use a more capable model when planning an application or tackling a difficult technical problem, then move to a faster and less expensive option when making smaller changes, fixing issues or continuing an established build.
Caffeine has previously allowed users to choose between several AI models, including GPT-6 Astra, GPT-6 Sol, Opus 5.5, Fable 5.1, Kimi K3, Sonnet 5 and DeepSeek V4.1 Flash. The platform says model selection is available across its plans.
OpenAI launched GPT-6 Sol and GPT-6 Luna on September 22, alongside the existing GPT-6 Astra. The company said improvements to caching and inference efficiency enabled lower pricing for the new models. GPT-6 Sol is listed at US$2 per million input tokens and US$10 per million output tokens through the API, compared with US$10 and US$50 respectively for GPT-6 Astra.
OpenAI’s model guidance places Astra at the top of its current GPT-6 range for the most demanding reasoning and coding work, while Sol is aimed at balancing capability and cost. This makes Sol a different option rather than a replacement for Astra.
The addition also fits with Caffeine’s broader approach to model choice. The platform has been developing Caffeine Inference, which is intended to route tasks between models based on factors such as cost and response speed. Its latest model-selection option gives users more direct control, while automated model routing remains part of the platform’s development direction.
For people building applications through repeated cycles of development, GPT-6 Sol gives Caffeine another model option for the long stretch of a project, particularly when the work shifts from initial creation to adding features, making corrections and refining an application over time.
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