Multimodal
A model that processes more than one input modality — typically text + images, sometimes audio or video.
What is Multimodal?
A pure LLM only sees text. A multimodal model can also see images (vision), hear audio, or process video. GPT-4o, Claude Opus/Sonnet, and Gemini are all natively multimodal — you can hand them an image and a question in the same prompt.
Common production use cases: **document understanding** (PDFs with figures + text), **vision QA** (describe what is in this image), **OCR + reasoning** (read a scanned form and answer questions about it), **chart interpretation** (analyse a graph).
Multimodal capability is the underrated step-change of 2024–25. Use cases that needed bespoke vision + NLP pipelines now collapse to a single API call.
Multimodal LLMs eliminate entire engineering pipelines. Every Indian Gen AI engineer should know how to use them.
A Hyderabad insurance startup replaced their 6-component KYC pipeline (OCR + document classifier + face detection + fraud checks) with a single Claude multimodal call. Throughput rose 3x, accuracy held, engineering surface dropped 80%.
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