Multimodal Model
In one line
A multimodal model is an LLM that can take in and reason over more than just text — typically combining images, audio or video alongside written prompts.
Going deeper
Multimodal models accept inputs beyond text — images, audio and increasingly video. GPT-4o, Claude's vision capability and Gemini are the canonical examples. Uploading a photo and chatting about it (or having a voice conversation) is becoming a default flow.
The marketing implication is that AI now reads your visual assets too. Product photos, logos and packaging are all part of the GEO surface. Clean alt text, accurate captions and legible on-pack copy take on new weight.
Multimodal support varies by model and by surface, so 'are we visible in visual search' is a question you have to answer per platform, not globally.
Related terms
LLM
A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.
LLMGPT
GPT (Generative Pre-trained Transformer) is OpenAI's family of Transformer-based LLMs — the engine behind ChatGPT and the de-facto baseline of the current AI market.
LLMClaude
Claude is Anthropic's LLM family, known for safety alignment, long-context handling and strong tool use — widely adopted in enterprise and developer settings.
LLMEmbedding
An embedding is a numeric vector representation of text or other data that preserves semantic meaning — the foundation of semantic search, vector databases and RAG.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
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