GPT-5 is designed to process and combine multiple input types within a single request, including text, images, and code. This means you can send a prompt that contains written instructions along with an uploaded image or snippet of code, and the model will consider all of them together when generating its response. For example, a developer could upload a screenshot of a web page layout issue and pair it with a text prompt asking for CSS fixes. GPT-5 will interpret the visual elements from the image alongside the written request, then provide a relevant solution. This ability is useful for debugging, document analysis, data extraction, and other workflows where information exists in more than one format.
For code-related tasks, GPT-5 can parse code snippets in many programming languages, identify errors, explain how the code works, and suggest improvements. This is enhanced by its improved reasoning capabilities, allowing it to handle multi-file or multi-step development tasks more accurately than previous models. When multimodal inputs include both code and supporting documentation—such as a README file or architecture diagram—the model can cross-reference them to produce a more context-aware answer. This is particularly useful for large codebase navigation or integrating new features into existing software.
GPT-5’s multimodal support is available both in ChatGPT and through the API. In a ChatGPT session, you might drag and drop an image of a chart, then ask the model to interpret it, summarize trends, and generate code to reproduce the chart programmatically. In the API, you can send structured requests that contain different input types as separate parts of the same message. The model processes them together, which allows developers to design richer, more interactive applications that blend textual instructions, visual data, and executable code into one unified workflow.