What is multimodal AI?

Multimodal AI refers to models that can process and generate multiple types of data, including text, images, audio, and video. Instead of being limited to one medium, multimodal models can look at a photo and describe it, listen to speech and transcribe it, or combine information from different sources to produce richer, more useful outputs.

The world is not just text. When you look at a restaurant menu, you see layout, images, fonts, and text all at once. When you attend a meeting, you process speech, facial expressions, slides, and shared documents simultaneously. Multimodal AI aims to handle information the way humans do: across multiple channels at once.

For developers and businesses, this means building applications that can work with real-world data in all its messy, multi-format glory rather than being limited to clean text inputs.

Multimodal models typically use specialized [encoders] for each type of input. An image encoder processes visual information into a numerical representation. An audio encoder does the same for sound. These representations are then mapped into a shared space where the language model can reason about them alongside text.

The key insight is that once different types of data are converted into compatible representations, the model can learn relationships between them. It learns that a picture of a sunset corresponds to words like "sunset," "orange sky," and "evening." It learns that a spoken phrase corresponds to specific text.

Some models are trained multimodal from the start. Others are built by adding visual or audio capabilities to an existing language model. Both approaches can work well, but natively multimodal models tend to have tighter integration between modalities.

Vision is the most mature multimodal capability. Current models can:

• ▸[Describe images]: Generate detailed descriptions of photos, diagrams, and illustrations
• ▸[Answer questions about images]: "What brand is the laptop in this photo?" or "How many people are in this image?"
• ▸[Read text in images (OCR)]: Extract text from screenshots, documents, receipts, and signs
• ▸[Analyze charts and diagrams]: Interpret data visualizations, flowcharts, and technical diagrams
• ▸[Compare images]: Identify differences between two versions of a design or document
• ▸[Understand spatial relationships]: Describe where objects are in relation to each other

Models with strong vision capabilities include OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, Google's Gemini (which was designed multimodal from the ground up), and open-source models like LLaVA and InternVL.

Audio understanding has advanced rapidly:

• ▸[Speech recognition]: Convert spoken language to text with high accuracy across many languages
• ▸[Speaker identification]: Distinguish between different speakers in a conversation
• ▸[Tone and sentiment]: Understand not just what was said but how it was said
• ▸[Music understanding]: Identify instruments, genres, and musical elements
• ▸[Sound classification]: Recognize environmental sounds like alarms, traffic, or appliances

OpenAI's GPT-4o has native audio understanding and generation. Google's Gemini also supports audio inputs. Whisper, OpenAI's open-source speech recognition model, has become a widely used standard. Meta's SeamlessM4T handles multilingual speech translation.

For audio output, several models can now generate natural-sounding speech, making voice-based AI applications increasingly practical.

Video understanding is the newest frontier in multimodal AI. It involves processing sequences of frames along with audio to understand what is happening over time.

Current capabilities include:

• ▸[Scene description]: Summarize what happens in a video clip
• ▸[Action recognition]: Identify specific actions or events
• ▸[Temporal reasoning]: Understand the sequence of events
• ▸[Visual question answering]: Answer questions about video content

Google's Gemini has been particularly strong in video understanding, with the ability to process long video inputs. Other providers are actively developing similar capabilities. Video understanding is computationally expensive because it involves processing many frames, so costs and latency are higher than for image or text tasks.

There is an important distinction between models that can [receive] multiple types of data and models that can [generate] multiple types of data.

[Multimodal input] (understanding): Most current models excel here. You can send an image and get a text description. You can send audio and get a transcript. The model takes in diverse data and produces text output.

[Multimodal output] (generation): Fewer models can generate across modalities. Some can produce images from text (like DALL-E or Stable Diffusion). Some can produce speech from text. Truly unified models that can fluidly generate text, images, and audio in a single response are still emerging.

GPT-4o was designed as a step toward unified multimodal output, with the ability to generate text, audio, and images. The trend across the industry is clearly moving toward models that can both understand and generate across all modalities.

Here is a snapshot of the multimodal landscape:

[GPT-4o] (OpenAI): Natively multimodal across text, vision, and audio. Strong all-around performance with real-time audio conversation capability.

[Claude 3.5 Sonnet and Claude 3 Opus] (Anthropic): Excellent vision capabilities, particularly strong at document understanding, chart analysis, and detailed image description.

[Gemini] (Google): Designed multimodal from the start. Supports text, images, audio, and video. Particularly notable for long-context multimodal understanding.

[LLaVA and open-source alternatives]: Community-built multimodal models that bring vision-language capabilities to open-source. Models like InternVL, Qwen-VL, and CogVLM offer competitive performance with the flexibility of open weights.

[Meta's models]: Llama-based multimodal variants and specialized models like ImageBind that connect multiple modalities.

[Healthcare]: Analyze medical images alongside patient records. Describe X-rays, flag anomalies in scans, and combine visual data with text-based medical history.

[Retail and e-commerce]: Understand product images, generate descriptions, power visual search ("find me a dress like this"), and analyze customer-submitted photos for reviews or support.

[Education]: Process textbook images, diagrams, and handwritten notes. Create accessible descriptions of visual content for students with visual impairments.

[Manufacturing and quality control]: Inspect products visually, identify defects, and generate reports combining image analysis with production data.

[Content creation]: Generate images from descriptions, create presentations that combine generated text and visuals, and produce video summaries.

The industry is clearly moving toward unified models that handle all modalities natively. Rather than having separate models for text, images, and audio, the goal is a single model that fluidly works across all types of data, just as humans naturally combine seeing, hearing, reading, and speaking.

This convergence will simplify application development, reduce the need to stitch together multiple specialized models, and enable new types of applications that were not possible when each modality lived in its own silo. We are still in the early stages of this convergence, but the direction is clear and the progress is rapid.