Top 10 Best Multimodal AI Models You Should Know
Multimodal models are AI systems that simultaneously process and integrate multiple data types.
Read the entire series
- Introduction to LangChain
- Getting Started with LlamaIndex
- How to build a Retrieval-Augmented Generation (RAG) system using Llama3, Ollama, DSPy, and Milvus
- Build AI Apps with Retrieval Augmented Generation (RAG)
- Exploring the Frontier of Multimodal Retrieval-Augmented Generation (RAG)
- Top 10 Best Multimodal AI Models You Should Know
Introduction
Artificial intelligence has made huge strides over the past few years, and one of the most exciting developments is the rise of multimodal models. These models go beyond handling just one type of data—like text, images, or audio—by combining them to create smarter, more intuitive systems. This shift allows AI to interact with the world in ways that mimic human understanding, making it much more versatile.
Multimodal models have become essential in AI because they offer new ways to process and generate insights from multiple data sources at once. From AI assistants that can respond to spoken commands and visual input to advanced systems that can learn by integrating different types of sensory data, multimodal AI is pushing boundaries.
In this post, we will explore the top 10 multimodal models worth knowing about. Whether you're a developer, researcher, or someone curious about AI, this list will give you a solid grasp of the most important models and their applications.
What Are Multimodal Models?
Multimodal models are AI systems that simultaneously process and integrate multiple data types. Instead of just handling text or images, they can combine inputs like audio, text, and video to produce more accurate and insightful results.
Take OpenAI’s DALL·E, for example. This model combines images with text descriptions to generate new images based on a prompt. If you give it a text prompt like “a cat wearing a spacesuit,” DALL·E will generate an image that matches that description. It can link what it understands from language (the description) with its knowledge of how objects look (the image generation), which is something text-only models can’t do.
The idea behind multimodal models has evolved over the years. Initially, AI systems were specialized for different tasks: some (e.g., BERT) handled language, while others worked with images or audio. But more recently, thanks to advances in AI architecture, we’ve been able to merge these capabilities into a single system. This shift has opened up new possibilities, allowing AI to work in more complex environments where information comes from multiple sources.
The real power of multimodal models is in how they mimic the way humans process information. Think about how we naturally combine what we hear, see, and read to understand a situation. Multimodal models aim to do the same—processing multiple input types to make smarter decisions or generate better responses. This makes them incredibly useful in areas like autonomous systems, virtual assistants, and healthcare, where understanding comes from multiple data streams.
Comparing Large Language Models (LLMs) and Multimodal Models
Most of us are familiar with large language models (LLMs), like OpenAI’s GPT-3 and Google’s BERT, which are great at understanding and generating text. LLMs have transformed how we interact with AI in chatbots, content generation, and language translation. But, they’re limited to just one type of input—language.
Multimodal models, on the other hand, extend beyond language processing. They can take multiple input forms—like combining images with descriptions or analyzing audio with video—to create richer, more comprehensive outputs.
For instance, compare GPT-3 with DALL·E:
GPT-3, an LLM, can generate text based on prompts like "Write an essay about AI," but that’s where it stops—it’s all text-based.
DALL·E, on the other hand, can take that same text prompt and generate a visual representation. This combination of language understanding and image generation makes it much more versatile for tasks requiring textual and visual information..
In the following sections, let’s explore the top 10 best multimodal models.
1. OpenAI GPT-4V
OpenAI GPT-4V is an advanced version of OpenAI's GPT-4 model, enhanced with multimodal capabilities that allow it to process and generate information from both text and images. The "V" in GPT-4V indicates the model's visual capabilities, making it a powerful tool for tasks that require an understanding of both written language and visual data. Additionally, GPT-4V has voice capabilities, which can receive voice input and convert it into text for further processing. By the same token, it can generate spoken responses to input prompts in various human-like voices.
Key Features and Capabilities:
Textual and visual input processing and output generation.
Advanced voice capabilities that allow it to process and generate spoken languages.
Its advanced image recognition feature can interpret complex visual cues and provide detailed answers.
Adeptly handles multimodal use cases such as image captioning, visual question answering, and scene description.
Multilingual input support for 26 languages.
2. OpenAI GPT-4o
GPT-4o is OpenAI's latest multimodal model, designed to process and generate text, audio, images, and video in real time. It combines text, vision, and audio capabilities into one integrated model, making it faster and more efficient than previous models. GPT-4o can respond to audio inputs almost instantly and performs equally well on tasks like reasoning and coding, with improved multilingual and audiovisual capabilities. It’s 50% cheaper and twice as fast as the GPT-4 Turbo, making it highly practical for developers.
To make their models more secure, OpenAI employed external red-teaming, i.e., hired independent contractors to conduct risk assessments and thoroughly test their model’s propensity to output harmful or biased information. Regarding accessibility, OpenAI also released a lightweight version of the model, GPT-4o-mini, which is more powerful than GPT 3.5 Turbo despite requiring fewer resources.
Key Features and Capabilities:
Considered the current state-of-the-art (SOTA) for multimodal models.
Average response time of 320 milliseconds, with response speeds as low as 232 milliseconds - comparable to human response times in conversations
Multilingual Support for over 50 languages; capable of seamless language switching during conversations.
Watch our YouTube video below to learn how to build a multimodal RAG with GPT-4o and the Milvus vector database.
3. OpenAI DALL-E 3
DALL-E 3 is OpenAI's latest image generation model, integrated with ChatGPT to allow users to create detailed images from text prompts with enhanced understanding of user intent. It builds on the advancements of previous DALL-E versions, featuring improved capabilities in producing coherent and creative images. DALL-E 3 can generate highly detailed, contextually accurate visuals and is designed to follow complex prompts with minimal misinterpretations, giving users more control over the content and style of the generated images.
One of the DALL-E family’s key innovations is using a discrete latent space, i.e., discrete tokens, to represent data, similar to how words are represented by tokens in LLMs instead of continuous vectors. This enables DALL-E 3 to learn a more structured and stable representation of generated images, resulting in better output.
Key Features and Capabilities
Efficient handling of complex prompts and detailed image generation
Standard and HD image quality options
Three available image sizes: 1024x1024, 1792x1024, and 1024x1792
Two distinct image generation styles: Natural and Vivid; with Natural being more realistic (and similar to images produced by DALL-E 2) , while Vivid is more ‘hyper-real’ and cinematic.
Strong emphasis on ethics and safety, with included guardrails that prevent the model from generating offensive or violent images, including:
Real-Time Prompt Moderation: analyzes prompts for harmful content and alerts the user accordingly.
Prompt Modification or Rejection: if an offensive prompt is detected, it can either reject the prompt or modify it
Post-Generation Filtering: If an image is determined to be potentially offensive, DALL-E 3 can stop showing it to the user.
4. Google Gemini
Gemini is Google's latest multimodal AI model and can integrate several modalities, including text, images, audio, code, and video. While the conventional approach to multimodal model development includes training separate networks for each modality and then fusing them together, Gemini was designed to be natively multimodal, pre-trained on different data types from the start.
Google has developed three versions of Gemini:
Gemini Nano: a lightweight model for mobile devices.
Gemini Pro: capable of a wide range of tasks and designed for large-scale deployment.
Gemini Ultra: the largest model designed for tackling highly complex, resource-intensive tasks. The Ultra exceeds current state-of-the-art results on 30 of the 32 most widely-used evaluation benchmarks.
Key Features and Capabilities
Creative and expressive capabilities include art and music generation, multimodal storytelling, and language translation.
Capable of analyzing data from multiple sources to verify output
Scoring 90%, Gemini Ultra is the first model to outperform human experts on the Massive Multitask Language Understanding (MMLU) benchmark, which tests world knowledge and problem-solving abilities across 57 domains.
Integrated with Google’s ecosystem of tools, services, and extensive knowledge base.
Gemini is also notable for its extended context window, with the Gemini 1.5 Pro model supporting up to 10 million tokens and enabling multimodal data processing. Its ability to handle such long contexts has sparked discussion about whether retrieval augmented generation (RAG), a method used to enhance LLMs' knowledge, could become obsolete in the face of long-context models.
For more insights and debate, check out our post: Will RAG Be Killed by Long-Context LLMs?
5. Meta ImageBind
Meta’s ImageBind stands out among multimodal models due to two key innovations. First, it uses a unified embedding space to interpret sensory data from an image, similar to how humans perceive multiple elements simultaneously. This ‘binding’ of different modalities enables a comprehensive understanding of inputs. Secondly, ImageBind supports six distinct modalities: text, audio, visuals, movement, thermal, and depth data, making it a highly versatile tool for complex multimodal tasks.
Key Features and Capabilities
Supports six types of modal data: text, visual, audio, visual, 3D depth, thermal, and movement (inertial measurement units (IMU)).
Can ‘upgrade’ other AI models to support input from any of the six modalities, enabling audio-based search, cross-modal search and generation, and multimodal arithmetic.
Adept at cross-modal retrieval and multimodal classification.
6. Anthropic Claude 3.5 Sonnet
Anthropic has recently upgraded its mid-range model, Sonnet, from Claude 3 to 3.5, making it the most advanced in its category. The new Claude 3.5 Sonnet offers enhanced vision capabilities, including superior verbal reasoning and the ability to transcribe from imperfect images. Despite this boost in performance, Anthropic continues to prioritize AI safety and ethics. The model isn't trained on user-submitted data to ensure privacy, and while its abilities have increased, it remains at ASL-2 on the AI Safety Levels (ASL) scale. Learn more about ASL on this blog page.
Key Features and Capabilities
Capable of processing text, images, and code.
Impressive coding ability, with a 92% score on the HumanEval coding benchmark
Strong mathematical capabilities, scoring 96% and 91.6% on the Grade School Math Grade (GSM8K) and Multilingual Math benchmarks, respectively
Artifacts feature places generated content in its own dedicated window, for a dynamic, better-organized workspace.
7. LLaVA
Introduced in the research paper Vison Instruction Tuning (Liu et al, 2023), LLaVA (Large Language and Vision Assistant) is a multimodal model that combines the open-source LLM Vicuna with a vision encoder for image and language processing. It integrates visual data and language understanding to create rich, interactive responses based on visual inputs. LLaVA is particularly useful for tasks like image captioning, visual question answering, and reasoning about images in combination with textual data. By bridging the gap between language and vision, LLaVA provides a more versatile, context-aware AI experience that can handle complex, real-world applications where visual and textual data interact.
LLaVA is the result of a joint research project conducted by Microsoft, Columbia University, and the University of Wisconsin-Madison. It was developed using visual instruction tuning, a technique by which an LLM is fine-tuned to understand and process prompts from visual cues. This connects language and vision, allowing it to understand instructions involving both modalities.
Key Features and Capabilities
Proficient at Image captioning, optical character recognition (OCR), visual question answering, and visual reasoning.
LLaVa-Med is the first multimodal model tailored for the healthcare industry
Achieved 92.5% accuracy when fine-tuned for ScienceQA, a diverse benchmark containing over 21,000 questions.
8. NExT-GPT
Developed by the University of Singapore, NExT-GPT is labelled as an “end-to-end general-purpose any-to-any MM-LLM system,”—meaning it can produce outputs in combinations of text, images, audio, and video and process them as inputs.
NExT-GPT was created by connecting Meta’s ImageBind as an encoder that allowed it to process 6 modalities with an LLM (Vicuna, as with LLaVA). From there, the LLM passes its output to a different diffusion decoder for each modality, fusing the outputs from each decoder to produce the final result.
Key Features and Capabilities
Capable of both receiving input and generating output in any combination of text, image, audio, and video modalities.
Components include Vicuna LLM and Meta’s ImageBind
Utilizes existing diffusion models for each modal generation: Stable Diffusion for images, AudioLDM for audio, and Zeroscope for video
9. Inworld AI
Inworld AI stands apart from the other models on this list as an engine for creating AI-driven virtual characters. As well as enabling the creation of more realistic non-playable characters (NPCs), Inworld can imbue virtual tutors, brand representatives, and various other characters with personalities for more immersive and authentic digital experiences.
Key Features and Capabilities
Integrates speech, text, and behavioral inputs for realistic interactions.
Create autonomous, emotionally responsive characters with distinct personalities and memories of prior interactions.
A comprehensive library of modular AI components, or primitives, can be assembled to suit various use cases.
Input primitives for enhancing digital experiences, including those for processing voice, vision, and state awareness, and recognition,
Output primitive for streamlined game and application development, including modules for text, voice, shape (2D and 3D), and animation assets.
AI logic engines and processing pipelines for increased gameplay complexity and enhanced functionality.
Multilingual Support (English, Japanese, Korean, Mandarin) includes text-to-speech capabilities, automatic speech recognition, and a selection of expressive voice outputs; additionally, cultural references change according to the target market.
10. Runway Gen-2
The Runway Gen-2 is distinctive for being the only multimodal model featured here specializing in video generation. Users can create video content through simple text prompts, by inputting an image, or even by using a video as a reference. Additionally, powerful features such as storyboard, which renders concept art into animation, and stylization, which transfers a desired style to every frame of your video, empower content creators to bring their ideas to life faster than ever.
Key Features and Capabilities
Text-to-video, image-to-video, and video-to-video prompt functionalities
Edit videos through tools such as Camera Control, allowing you to control the direction and intensity of shots, and Multi-Motion Brush, which lets you apply specific motion and direction to objects and areas within a scene
iOS app available for smartphone content generation
Summary
The table below provides an overview of the top 10 multimodal models.
| Model | Vendors/Creators | Key Capabilities |
| GPT-4V | OpenAI | Text and image processing; understands speech commands and can output spoken output |
| GPT-4o | OpenAI | Text, image, audio, and video processing |
| DALL-E 3 | OpenAI | Text and image processing; image output only |
| Gemini | text, images, audio, code, and video processing | |
| ImageBind | Meta | Supports six types of modal data: text, visual, audio, visual, 3D depth, thermal, and movement |
| Claude 3.5 Sonnet | Anthropic | Capable of processing text, images, and code |
| LLaVA | Microsoft, Columbia University, University of Wisconsin-Madison | Text and image processing; LLaVA-med fine-tuned for the medical industry |
| NExT-GPT | University of Singapore | Capable of both receiving input and generating output in any combination of text, image, audio, and video modalities. |
| Inworld AI | Inworld | Engine for creating AI-driven virtual characters |
| Runway Gen-2 | Runway | Text-to-video, image-to-video, and video-to-video prompt functionalities |
Further Reading
We encourage you to continue reading the recommended posts about multimodal models and the types of applications you can develop with them.
Building a Multimodal Product Recommender Demo Using Milvus and Streamlit
How Vector Databases are Revolutionizing Unstructured Data Search in AI Applications
Exploring OpenAI CLIP: The Future of Multi-Modal AI Learning
Exploring the Frontier of Multimodal Retrieval-Augmented Generation (RAG)
Build Better Multimodal RAG Pipelines with FiftyOne, LlamaIndex, and Milvus
- Introduction
- What Are Multimodal Models?
- Comparing Large Language Models (LLMs) and Multimodal Models
- 1. OpenAI GPT-4V
- 2. OpenAI GPT-4o
- 3. OpenAI DALL-E 3
- 4. Google Gemini
- 5. Meta ImageBind
- 6. Anthropic Claude 3.5 Sonnet
- 7. LLaVA
- 8. NExT-GPT
- 9. Inworld AI
- 10. Runway Gen-2
- Summary
- Further Reading
Content
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