"When large language models first became widely available, you could only send text and get a text response back. But in just a few years, these models have become multimodal, meaning they can now process images, audio, and even real-time video streams. You can bring the same capabilities into your own LLM-powered apps and go beyond plain text. In this article, you'll learn how to build multimodal AI interactions using Next.js and Gemini."
"In AI, a modality refers to the kind of input or data you're dealing with, such as text, images, audio, video, or even sensor data. Traditional models were built to handle only one type at a time. For example, a text model processes only text, and an image model only sees pixels. Multimodal AI is different. It can understand and work with multiple types of input together. You can feed it a photo and ask a question about it."
"Popular multimodal models include those from OpenAI, Google's Gemini, Claude, and DeepSeek. All of them can process combinations of text, images, audio, and, in some cases, video. For this tutorial, we'll use the Gemini API because it's easier to set up and offers a generous free trial. Create Gemini API key Head over to Google AI Studio and click the Create API key button as shown below: Once created, copy the key and store it somewhere safe for now."
Multimodal AI enables models to process and combine text, images, audio, video, and sensor data for richer interactions. Models from OpenAI, Google's Gemini, Claude, and DeepSeek support multimodal inputs and can answer questions about images, compare transcripts to audio, and analyze uploads. A tutorial demonstrates building multimodal AI interactions using Next.js and Gemini, covering audio, images, video, and file uploads. Practical steps include creating a Gemini API key, cloning a starter repository, installing dependencies, and integrating Gemini into an app. The approach emphasizes using a starter project to focus on media handling and LLM integration rather than UI scaffolding.
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