Databutton Adds GPT-5 Support Across All Projects.

Databutton announced on LinkedIn that GPT-5 is now available to all users on its platform. The update allows developers to switch to OpenAI's latest language model in both new and existing projects immediately.

Users can enable GPT-5 through their profile settings. The company is collecting feedback through its Discord community to refine the integration.

The move gives Databutton users access to GPT-5's capabilities for building web applications through natural language. Developers can now choose between multiple model options when working on projects, similar to how platforms like Cursor and Replit offer model selection.

Databutton's approach differs from competitors by allowing model switching within existing projects rather than requiring new project creation. This enables developers to test different models against the same codebase and compare outputs directly.

The platform has recently expanded its model support strategy. Following their rollout of Gemini 2.5 Pro, Databutton now offers developers choice between major AI providers for different use cases.

Databutton's implementation allows developers to:

• Switch models via profile settings without project reconfiguration
• Apply GPT-5 to existing codebases for iterative development
• Test model performance across different application types

The company has not disclosed pricing changes associated with GPT-5 access or whether usage will be metered differently than GPT-4.

Developers can access the model switcher through their Databutton profile. The platform continues to support previous model versions, allowing teams to maintain existing workflows while experimenting with newer capabilities.

Databutton positions itself in the vibe coding category, competing with platforms like Lovable, v0, and Bolt. These tools generate functional web applications from natural language descriptions, with varying approaches to model selection and code generation.

The GPT-5 integration represents Databutton's strategy of rapid model adoption. By supporting new releases quickly, the platform gives users access to latest AI capabilities while maintaining backward compatibility with existing projects. This approach contrasts with platforms that stabilize on specific model versions for consistency.