The rapid advancement of artificial intelligence (AI) has led to a new era of models designed to process and generate data across multiple modalities. These include text, images, audio, and video. These multimodal models are increasingly used in various applications, from content creation to advanced analytics. This article will introduce you to the concept of multimodal models, and compare 7 of the most popular multimodal models (both open-source and proprietary) currently available. It will guide you on when and where to use each model based on its features, use cases, accessibility, and cost.
Multimodal models are specialized AI architectures designed to handle and integrate data from various modalities. They can perform tasks such as generating text from images, classifying images based on descriptive text, and answering questions that involve both visual and textual information. These models are typically trained on large datasets containing diverse types of data, allowing them to learn complex relationships between different modalities.
Multimodal models have become vital for tasks that require contextual understanding across different formats. For instance, they can enhance search engines, improve customer service through chatbots, enable advanced content generation, and assist in educational tools.
Learn More: Exploring the Advanced Multi-Modal Generative AI
The table below compares the modalities, strengths, cost, and other details of the 7 most popular multimodal models available today.
# | Model | Modality Support | Open Source / Proprietary | Access | Cost* | Best For | Release Date |
1 | Llama 3.2 90B | Text, Image | Open Source | Together AI | Free $5 worth of credits | Instruction-following | September 2024 |
2 | Gemini 1.5 Flash | Text, Image, Video, Audio | Proprietary | Google AI services | Starts at $0.00002 / image | Holistic understanding | September 2024 |
3 | Florence | Text, Image | Open Source | HuggingFace | Free | Computer vision strength | June 2024 |
4 | GPT-4o | Text, Image | Proprietary | OpenAI subscription | Starts at $2.5 per 1M input tokens | Optimized performance | May 2024 |
5 | Claude 3 | Text, Image | Proprietary | Claude AI | Sonnet: FreeOpus: $20/monthHaiku: $20/month | Ethical AI focus | March 2024 |
6 | LLaVA V1.5 7B | Text, Image, Audio | Open Source | Groq Cloud | Free | Real-time interaction | January 2024 |
7 | DALL·E 3 | Text, Image | Proprietary | OpenAI platform | Starts at $0.040 / image | Inpainting, high-quality generation | October 2023 |
*prices mentioned are updated as of October 21, 2024
Now let’s explore their features and use cases in more detail.
Meta AI’s Llama 3.2 90B is currently one of the most advanced and popular multimodal model being used. This latest variant of the Llama series combines instruction-following capabilities with advanced image interpretation, catering to a wide range of user needs. The model is built to facilitate tasks that require both understanding and generating responses based on multimodal inputs.
Gemini 1.5 Flash is Google’s latest lightweight multimodal model, adept at processing text, images, video, and audio, with great speed and efficiency. Its ability to provide comprehensive insights across different data formats, makes it suitable for applications that require a deeper understanding of context.
Florence 2 is a lightweight model from Microsoft, designed primarily for computer vision tasks while also integrating textual inputs. Its capabilities enable it to perform complex analyses on visual content. This makes it an invaluable model for vision-language applications such as OCR, captioning, object detection, instance segmentation, etc.
GPT-4o is an optimized version of GPT-4, designed for efficiency and performance in processing both text and images. Its architecture allows for quick responses and high-quality outputs, making it a preferred choice for various applications.
Claude 3.5 is a multimodal model developed by Anthropic, focusing on ethical AI and safe interactions. This model combines text and image processing while prioritizing user safety and satisfaction. It is available in three sizes: Haiku, Sonnet, and Opus.
LLaVA (Large Language and Vision Assistant) is a fine-tuned model. It uses visual instruction tuning to support image-based natural instruction following and visual reasoning capabilities. Its small size makes it suitable for interactive applications, such as chatbots or virtual assistants, that require real-time engagement with users. Its strengths lie in processing text, audio, and images simultaneously.
Open AI’s DALL·E 3 is a powerful image generation model that translates textual descriptions into vivid and detailed images. This model is renowned for its creativity and ability to understand nuanced prompts, enabling users to generate images that closely match their imagination.
Multimodal models are pushing the boundaries of AI by integrating various types of data to perform increasingly complex tasks. From combining text and images to analyzing real-time videos with audio, these models open up new possibilities in industries like healthcare, content creation, and virtual reality.
In this article, we have explored the features and use cases of 7 popular multimodal AI models. However, selecting the right model depends on the specific task at hand. Whether you’re generating images, analyzing diverse data inputs, or optimizing videos in real-time, there is a multimodal model specialized for it. As AI continues to evolve, multimodal models will include more data types for more complex and diverse use cases.
Learn More: What Future Awaits with Multimodal AI?
A. Multimodal models are AI systems that can process and generate data across multiple modalities, such as text, images, audio, video, and more, enabling a wide range of applications.
A. Multimodal models are helpful in applications that require understanding or generating data across different formats, such as combining text and images for enhanced context.
A. Traditional models typically focus on a single type of data (like text or images), whereas multimodal models can integrate and process multiple data types simultaneously.
A. The cost of a multimodal model can vary widely depending on the model, usage, and access method. However, some multimodal models are available for free or offer open-source options.
A. Most of the multimodal models discussed in this article are available through APIs or platforms such as HuggingFace.
A. Depending on the model, some may offer fine-tuning options, while others are primarily pre-trained and not meant for user-level customization.
A. Different multimodal models are built to handle different types of data. This may include text, image, video, and audio.