Multimodal LLMs: AI That Sees, Hears, and Reads

Key Takeaways

Beyond Text: Multiple Modalities

A multimodal AI model can process and generate multiple types of data simultaneously. Early LLMs were purely text-in, text-out. Modern frontier models, GPT-4o, Gemini 2.5, Claude 3.5 Sonnet, Llama 4 Maverick, can process images, describe what they see, analyze charts and diagrams, and reason about visual and textual information together in a single context.

Modalities include: text (obviously), images (static photos, diagrams, screenshots), audio (speech, music), video (sequences of frames), and structured data. The most widely deployed multimodal capability today is vision, virtually all frontier models now accept images as input. Audio understanding and video analysis are rapidly maturing.

How Vision Is Added to LLMs

The dominant approach is to use a separate vision encoder (typically based on CLIP or a similar vision-language pre-training model) to convert images into embeddings that the LLM can process. The image is divided into patches, each patch is encoded into a vector, and these vectors are projected into the LLM's embedding space where they're treated alongside text tokens.

This approach is efficient because it reuses a strong pre-trained visual encoder. The LLM learns, during multimodal fine-tuning, to ground its language generation in visual features. Llava, a popular open-source multimodal framework, demonstrated this approach, a modest visual encoder combined with Llama produced competitive vision-language models.

Native Multimodal Training

Google's Gemini and Meta's Llama 4 take a different approach: native multimodal training from the start. Rather than adding vision as a post-hoc capability, these models are trained from scratch on interleaved text, image, audio, and video data. This produces more deeply integrated understanding, the model doesn't think of images as 'add-ons' to text processing.

GPT-4o ('omni') also pursues native multimodality, processing audio, vision, and text with the same model architecture. This enables capabilities like real-time voice conversation with visual awareness, features that would be awkward or impossible with bolted-on multimodal approaches.

What Multimodal AI Enables

Vision: analyzing medical imaging alongside reports, describing products from photos, reading handwritten notes, understanding whiteboard diagrams, debugging UI from screenshots, accessibility descriptions for visually impaired users. Code from screenshots: take a photo of a UI mockup and generate the HTML/CSS, a common developer workflow.

Audio and video: real-time voice assistants with conversational awareness, video summarization and search, accessibility transcription, analyzing customer call recordings for sentiment and topics. Each new modality unlocks applications that were impossible or required specialized models, multimodal models collapse these into a single flexible system.