Multimodale KI
// Description
Multimodal AI refers to systems that can simultaneously process and generate multiple data types — text, images, audio, video, and code. Unlike pure text LLMs or specialized image generators, multimodal models combine different capabilities in one system.
Frontier models in 2026 are all multimodal: GPT-5.2 understands and generates text, images, audio, and video. Gemini 3.1 is natively multimodal — trained on all modalities from the start. Claude Opus 4.6 processes text, images, and code. Google's Veo and OpenAI's Sora are multimodal video models.
Why multimodality matters: marketing content is inherently multimodal — a social media campaign encompasses text, images, videos, and audio. Multimodal models enable coherent cross-format creation from a single prompt: a campaign concept is simultaneously generated as text briefing, visual mockup, and video storyboard.
Practical applications: image-to-text (analyses, alt texts, descriptions), text-to-image (DALL-E, Midjourney), text-to-video (Sora, Runway), text-to-audio (ElevenLabs), and increasingly "any-to-any" conversions. The boundaries between modalities are blurring rapidly.
// Use Cases
- Cross-format content creation
- Image analysis and description
- Video generation from text prompts
- Audio/voice generation
- Campaign mockups across all formats
- Accessible alt texts & audio description
- Product visualization from descriptions
- Multimedia chatbots
Multimodal AI is the game-changer for agencies — instead of five specialized tools, we increasingly use models that can do it all. Gemini is our strongest multimodal model, GPT-5.2 the best all-rounder.
// Frequently Asked Questions
What is multimodal AI?
Which models are multimodal?
Why is multimodality important for marketing?
// Related Entries
Need help with Multimodale KI?
We are happy to advise you on deployment, integration and strategy.
Get in touch