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Models & Platforms

Multimodal AI

AI systems that can process, understand, and generate across multiple types of data — text, images, audio, video, and code — within a single model.

Why it matters

Multimodal AI eliminates the need for separate OCR, speech-to-text, and image recognition pipelines. One model can handle workflows that previously required five different services.

Evolution

Early AI models were unimodal: text-only, image-only, or audio-only. Modern foundation models are natively multimodal — Claude, GPT-4, and Gemini can all reason about images alongside text. This isn't just "vision bolted on" — the models genuinely understand relationships between visual and textual information.

Practical capabilities

  • Image understanding — describe photos, read charts, extract data from screenshots.
  • Document parsing — process PDFs, invoices, and forms with mixed text/images.
  • Audio processing — transcription, translation, and audio understanding (Gemini, GPT-4o).
  • Code + visual — generate UI code from screenshots or wireframes.

Related terms

Large Language Model(LLM)A neural network trained on massive text corpora that can understand and generate human language, typically with billions of parameters.

On the AI Radar

Gemini 3.1 ProGoogle's flagship multimodal model family. Gemini 3.1 Pro is the model behind Google AI Overviews and Google AI Mode, and via Apple's January 2026 deal it's also the model behind the rebuilt Siri. That makes it the engine increasingly answering questions that used to go through Google Search, plus the one that will mediate iPhone queries.ClaudeAnthropic's flagship model family. When Claude searches the web, the answer comes back with the URLs it pulled from, which makes it the easiest answer engine to audit if you want to know what content of yours it actually trusts.