What are the 4 types of LLM?

The Four Types of Large Language Models: Beyond Just Text

When people hear "Large Language Model" (LLM), they often think only of models that generate or understand text—like chatbots or writing assistants. But the reality is broader. While traditional LLMs focus on text processing, advancements in AI have expanded the concept to include models that handle other modalities, effectively evolving the definition of what a "language" model can be.

The first and most well-known type is the text-processing LLM. These models, such as GPT or BERT, specialize in understanding and generating human language. They power everything from search engines to virtual assistants. But language isn’t just words on a page—sometimes it’s visual, auditory, or temporal.

This brings us to image-processing LLMs. Models like CLIP or Flamingo combine vision and language, interpreting images and describing them in natural language. Think of them as bilingual—they speak both pixels and prose.

Next, audio-processing LLMs bridge spoken language and text. Whisper by OpenAI, for example, transcribes speech with remarkable accuracy and even identifies language or detects voice patterns. These models don’t just hear—they understand context and meaning in sound.

Finally, video-processing LLMs handle sequences of images combined with audio, interpreting complex scenes over time. They’re used in automated content moderation, video summarization, and even filmmaking tools. These models capture narratives unfolding in time, making sense of motion, dialogue, and emotion.

So while the term “language model” might evoke only text-based systems, the modern landscape includes models that “speak” through images, sound, and video. The future of AI isn’t confined to one modality—it’s multimodal, adaptive, and increasingly human-like in its understanding.