Humans experience the world through multiple senses simultaneously -- we see a scene, hear sounds, and read text all at once, integrating these inputs seamlessly. Until recently, AI models were specialists: a vision model for images, a language model for text, and a speech model for audio. Multimodal LLMs change this by combining multiple input and output modalities into a single unified model. GPT-4V, Gemini, and Claude can now "see" images, understand documents, and reason about visual content alongside text.

What Makes a Model Multimodal?

A multimodal model can process and reason about information from more than one data type (or modality). The most common modalities include text, images, audio, and video. What distinguishes modern multimodal LLMs from earlier approaches is that they process these modalities in a unified framework, allowing cross-modal reasoning.

For example, you can show a multimodal LLM a photograph of a restaurant menu and ask it to recommend dishes for someone with dietary restrictions. The model must understand the visual layout of the menu, read the text within it, comprehend the food items, and apply knowledge about dietary needs -- all in a single inference pass.

"The most important shift in multimodal AI is not that models can process images -- it is that they can reason about images using the same capabilities they apply to text."

How Multimodal LLMs Process Images

The most common approach to building multimodal LLMs involves three components:

Visual Encoder

A pre-trained vision model -- typically a Vision Transformer (ViT) -- processes the input image and produces a sequence of visual tokens or embeddings. These represent the visual information in a format compatible with the language model's input space. Popular visual encoders include CLIP's vision component, SigLIP, and EVA-CLIP.

Projection Layer

A learned projection module maps the visual embeddings into the language model's embedding space. This can be as simple as a linear projection or as complex as a cross-attention mechanism like the Perceiver Resampler used in Flamingo. The projection layer is the bridge between visual understanding and language reasoning.

Language Model Backbone

The core language model processes the projected visual tokens alongside text tokens, treating them as part of the same sequence. This allows the model to apply its language understanding and reasoning capabilities to visual information. The language model is usually a pre-trained LLM like LLaMA, Vicuna, or a proprietary model.

Key Takeaway

Modern multimodal LLMs work by encoding images into the same representation space as text, allowing the language model to reason about visual information using its existing capabilities.

Key Players in the Multimodal Space

GPT-4V and GPT-4o

OpenAI's GPT-4 with vision (GPT-4V) was a landmark release that demonstrated state-of-the-art visual reasoning. GPT-4o extended this further by natively processing audio and images alongside text, enabling real-time multimodal conversations. These models set the standard for what multimodal AI can achieve in terms of complex visual reasoning and instruction following.

Google Gemini

Gemini was designed from the ground up as a multimodal model, unlike GPT-4V which added vision to an existing language model. Google claims this native multimodal architecture gives Gemini advantages in understanding the relationships between different modalities. Gemini Pro and Ultra process text, images, audio, and video, with particularly strong performance on document understanding tasks.

Claude (Anthropic)

Anthropic's Claude models include vision capabilities that emphasize careful, safety-conscious image understanding. Claude excels at detailed image analysis, document comprehension, and chart interpretation, with a focus on providing accurate, grounded descriptions rather than speculative interpretations.

Open-Source Multimodal Models

The open-source community has produced several impressive multimodal models. LLaVA (Large Language and Vision Assistant) demonstrated that combining a CLIP visual encoder with a LLaMA language model could achieve competitive visual understanding. InternVL, CogVLM, and Qwen-VL have pushed open-source multimodal performance even further, narrowing the gap with proprietary models.

Practical Applications

Multimodal LLMs have unlocked a range of applications that were previously difficult or impossible:

  • Document Understanding: Extracting information from invoices, receipts, contracts, and forms that combine text, tables, and images.
  • Visual Question Answering: Answering natural language questions about images, from simple identification to complex reasoning.
  • Accessibility: Describing images and visual content for visually impaired users with unprecedented detail and accuracy.
  • Medical Imaging: Analyzing X-rays, MRIs, and pathology slides alongside clinical notes for diagnostic support.
  • Creative Applications: Providing feedback on designs, analyzing artwork, and assisting with visual creative processes.
  • Code from Screenshots: Converting UI mockups and screenshots into functional code.

Challenges and Limitations

Despite impressive progress, multimodal LLMs face several significant challenges.

Visual Hallucinations

Just as text-only LLMs can hallucinate facts, multimodal models can hallucinate visual details. A model might describe objects that are not present in an image, misidentify spatial relationships, or incorrectly read text in a photograph. These visual hallucinations are particularly concerning for applications requiring high accuracy.

Spatial Reasoning

Current multimodal LLMs often struggle with precise spatial reasoning. They may have difficulty counting objects, understanding relative positions, or interpreting diagrams that require geometric understanding. This limits their usefulness for tasks like architectural analysis or scientific diagram interpretation.

Video Understanding

While some models can process video, true video understanding -- tracking objects over time, understanding causal relationships between events, and interpreting temporal dynamics -- remains a significant challenge. Most current approaches sample a few frames rather than processing full video streams.

Key Takeaway

Multimodal LLMs represent a fundamental shift toward AI that perceives the world more like humans do. While challenges remain in accuracy and spatial reasoning, the practical applications are already transformative across industries.

What Comes Next

The multimodal frontier is expanding rapidly. Future developments include models that can generate images and audio alongside text, true real-time video understanding, and integration with robotics for physical-world interaction. The ultimate vision is an AI that can perceive, understand, and interact with the world across all the modalities that humans use, creating a more natural and powerful interface between humans and AI systems.