The AI world’s getting crowded with models that can see, read, and understand multiple types of data at once. While everyone’s arguing about text models, multimodal AI is quietly reshaping how machines process information. Here’s the breakdown of what matters in 2024.
1. H2OVL Mississippi
Fresh out of Mountain View, this 2.1B parameter model (and its 0.8B parameter brother) is crushing document processing tasks, ranking as the #1 SLM for text recognition.
Its secret is breaking images into 448×448 pixel tiles and turning them into structured data. On OCRBench it’s beating models 20x its size. The best part is it runs locally, so your data stays put.
2. GPT-4V (OpenAI)
This is OpenAI’s visual powerhouse. It can analyze anything from handwritten notes to complex diagrams, and even understands memes. The model shows remarkable reasoning abilities, like being able to debug code from screenshots or explain complex technical diagrams.
3. Gemini Ultra (Google)
Google’s answer to GPT-4V. Trained on multimodal data from scratch rather than bolted-on vision features. Currently leading 30/32 industry benchmarks for multimodal tasks. Especially strong at coding tasks where visual context matters.
4. ImageBind (Meta)
Meta’s six-modal monster processes text, images, audio, thermal data, depth maps, and motion data — all at once. Open source too. Think of it as giving AI all five human senses plus a few extras.
5. Claude 3 Series (Anthropic)
The analytical one. Unlike models that just spit out answers, Claude 3 breaks down its visual reasoning step by step. Particularly strong at understanding charts, graphs, and technical documentation. Its explanations actually make sense.
6. GroundingDINO
Zero-shot object detection champion. Point it at an image and ask about any object — it’ll find it. No training needed. Researchers love it for its flexibility and precision in identifying objects it’s never seen during training.
7. PaliGemma (Google)
Google’s specialized vision-language model. Smaller than Gemini but faster. Excels at quick visual Q&A tasks and basic image understanding. Good middle ground between heavyweight models and lightweight solutions.
8. CogVLM
One of the current leaders in visual question answering benchmarks. Give it an image and ask anything — it understands context, spatial relationships, and can even make logical deductions about what it sees.
9. LLaVA
Open source competitor to GPT-4V. Combines Vicuna’s language skills with CLIP’s visual understanding. Not as powerful as commercial options but free and hackable. Community’s constantly improving it.
10. Runway Gen-2
The video specialist. Turns text descriptions into actual videos. Still experimental but showing promise for creative applications. First of its kind to handle temporal understanding well.
11. QwenVL (Alibaba)
Multilingual vision-language model that’s particularly strong in Asian languages. Handles complex documents in multiple languages and scripts. Growing fast in Asian markets.
12. Florence-2 (Microsoft)
Microsoft’s lightweight vision-language model focused on efficiency. Good at connecting visual and textual information without massive compute requirements. Popular for practical applications.
13. BLIP v2 (Salesforce)
Research powerhouse that’s advancing how models learn visual concepts. Strong at zero-shot tasks and transfer learning. Many newer models build on its innovations.
14. SigLIP
Specialized in image embedding — turning pictures into numbers that capture their meaning. Not flashy but crucial for search and recommendation systems. Powers lots of real-world applications.
15. Stable Video Diffusion
Text-to-video model from Stability AI. Different approach from Runway Gen-2, focused on transforming existing images into videos. Growing fast, especially in creative industries.
The field’s moving quick — a new model could drop tomorrow and shake up this whole list. But right now, these 15 represent the cutting edge of what’s possible when AI starts using multiple senses at once.