Developers can integrate GPT-5 by using the OpenAI API, which provides multiple variants—gpt-5, gpt-5-mini, and gpt-5-nano—as well as a gpt-5-chat-latest option optimized for conversational interfaces. The core workflow is straightforward: send a request to the API endpoint with your chosen model, input text (and optionally images or audio), and receive the model’s response. This flexibility allows GPT-5 to fit into a wide range of applications, from backend data analysis to customer-facing chat tools. For example, you might connect gpt-5-mini to a support chatbot for faster, lower-cost responses, while reserving gpt-5 for complex problem-solving in an engineering assistant tool.
In addition to text-only inputs, GPT-5 can process multimodal data, meaning you can combine text with images or audio in the same request. This is useful for scenarios like uploading a screenshot of an error message along with a question about how to fix it. GPT-5 can also be combined with tools you define in your own application. For example, you could let the model call a “getWeather” function in your system, retrieve live data, and then integrate that into its final answer. The API supports parameters such as reasoning_effort, which lets you control how much computation the model dedicates to a problem, and verbosity, which controls the level of detail in responses. These settings make it easier to balance cost, latency, and accuracy depending on the use case .
When deploying GPT-5, it’s common to pair it with a retrieval-augmented generation (RAG) setup to ensure answers are grounded in your own data. In this architecture, your application fetches relevant documents from a database or vector store and passes them to GPT-5 as context. Because GPT-5 is more efficient with tokens than earlier models, it can handle larger context windows without sacrificing speed. Developers can also stream partial responses back to the user for a smoother experience—particularly valuable when the model is performing high-effort reasoning. This combination of multimodal inputs, tool use, parameter controls, and integration with your data sources allows GPT-5 to fit seamlessly into a variety of production environments.