Which AI models handle multimodal input best?
Top multimodal models
As of 2025, several models stand out for multimodal input. OpenAI's GPT-4o handles text, images, and audio natively, with strong performance across all three. Google's Gemini 1.5 Pro supports text, images, audio, and video, with a very large context window. Anthropic's Claude 3.5 Sonnet is excellent with text and images, and is often praised for detailed image understanding.
Other options include Meta's Llama 3.2 (open-source, text and images) and various specialized models for tasks like OCR or video analysis. The 'best' model depends on whether you need real-time audio, video understanding, or just image captioning.
How to choose
Start by listing the input types you actually need. If you only need text and images, many models will work well. If you need audio or video, your choices narrow. Also consider output modalities: some models can generate images or speech, not just understand them.
Check the context window size—how much data the model can process at once. For video or long documents, a larger context is essential. Finally, test with your own examples, because performance varies by domain (e.g., medical images vs. natural photos).
- GPT-4o: text, images, audio
- Gemini 1.5: text, images, audio, video
- Claude 3.5 Sonnet: text, images
- Llama 3.2: text, images (open-source)
- Consider specialized models for niche tasks
Common mistakes
- Assuming all multimodal models handle all modalities equally well—they don't.
- Overlooking the cost and latency of processing large media files.
- Forgetting that some models only accept multimodal input but output only text.
