10 Open Multimodal Models

Why does open source matter in AI? There are many advantages to open-source models. You can deploy models from a browser, customize them for your own needs, and be assured of your privacy. It gives you an opportunity to explore models freely and gain hands-on experience with AI. Open models also benefit from insights from the global developer community, often leading to quicker bug fixes, new features, and performance improvements.

Multimodal models (MLLMs) are gaining more attention as researchers focus on achieving strong multimodal capabilities, not only for text but also for advanced vision reasoning.

Today, we offer you a list of the 10 most powerful open MLLMs of various sizes, so you can find the best option for your specific needs:

1. MiniCPM-Llama3-V 2.6 is a powerful compact 8-billion-parameter model excelling in image, multi-image, and video understanding. It outperforms other models in single-image and video analysis, offering fast, energy-efficient processing. It supports OCR, multiple languages, and is easy to deploy locally or online.

2. Florence-2, a Microsoft vision foundation model, excels in vision and vision-language tasks like captioning and object detection. Trained on 126 million images with 5.4 billion annotations, it performs well in zero-shot and fine-tuned applications.

3. OmniParser is a Microsoft tool that converts UI screenshots into a structured format, helping improve LLM based UI agents. It’s trained on two datasets: one to detect clickable icons on web pages and another to describe each UI element's function. OmniParser works on PC and phone screenshots but requires human judgment for best results.

4. Meta's Llama 3.2-Vision family features 11B and 90B LLMs for visual recognition, image reasoning, and captioning. Based on Llama 3.1, it includes a vision adapter and is fine-tuned with supervised and reinforcement learning, outperforming many models on industry benchmarks.

5. Qwen2-VL excels at interpreting images of various resolutions, supports videos over 20 minutes for interactive tasks, and can operate devices like phones and robots using complex reasoning. It supports multiple European and Asian languages. Available in 2B, 7B, and 72B model sizes.

6. NVLM 1.0 by NVIDIA is designed for tasks like optical character recognition, reasoning, and coding. This decoder only model outperforms many top models like GPT-4o in vision-language tasks and improves on its text-only backbone. It's open-source on Hugging Face, with reproducible benchmarks and multi-GPU support for efficient inference.

7. Phi-3.5-vision is a compact, state-of-the-art Microsoft model that includes features for general image understanding and comparison, optical character recognition, and video summarization. Supporting up to 128K tokens, it’s optimized for precise instruction-following and safety.

8. Idefics2 is a Hugging Face MLLM that can answer questions, describe visuals, and create stories from multiple images. It has strong abilities in OCR, document understanding, and visual reasoning, but is not suitable for high-stakes decision-making or sensitive content. It has three versions, but the most popular one is Idefics2-8B.

9. LLaVA-V1.5, built on LLaMA/Vicuna with GPT-generated data, is ideal for research in vision-language applications and chatbots. It was trained on a mix of image-text pairs and academic data and tested on 12 benchmarks. It has 7B and 13B versions.

10. Janus-1.3 B by DeepSeek AI is now the most popular any-to-any model that separates visual encoding into different pathways, which helps it manage text and image tasks more effectively while using a single transformer framework. This separation makes Janus more flexible.