5 Best Multimodal AI Tools for 2024: Which Ones Should You Use?

KEY TAKEAWAYS

Today, multimodal AI tools and language models can interact with and identify text, images, video, and audio. Which ones are best for you?

(LLMs) are going way beyond the days of unimodal input — models that are designed to perform a particular task, such as image processing and speech recognition.

Today, multimodal AI tools and language models can interact with and identify text, images, video, and audio.

Research from Markets and Markets research estimates that the global market for multimodal AI will grow from $1 billion in 2023 to $4.5 billion by 2028.

One of the core reasons for this growth is that multimodal LLMs support a much wider range of tasks than language-centric LLMs, from giving users more variety in the type of input they can enter and the output they receive.

But with a widening choice out there, it isn’t easy to know which tool to use for a given purpose, so join us as we look at… 

The 5 Best Multimodal AI Tools For 2024

5. Google Gemini

Google Gemini is a natively multimodal LLM that can identify and generate text, images, video, code, and audio. Gemini comes in three main versions: Gemini Ultra, Gemini Pro, and Gemini Nano. 

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Gemini Ultra is the largest LLM, Gemini Pro is designed to scale across multiple tasks, and Gemini Nano is designed for efficiency for on-device tasks, making it ideal for mobile device users. 

Gemini can reason out answers to visual questions
Gemini can reason out answers to visual questions.

Since its release, Gemini has shown some promising performance. According to CEO and co-founder of Google DeepMind, Demis Hassabis, Gemini has outperformed GPT-4 on 30 out of 32 benchmarks. 

In addition, Gemini has also become the first language model to outperform human experts on massive multitask language understanding (MMLU) and has achieved a state-of-the-art score on the MMMU benchmark, which measures performance in multimodal tasks. 

4. ChatGPT (GPT-4V)

GPT-4V or GPT-4 with vision is a multimodal version of GPT-4 that enables users to input text and images into ChatGPT. Now, users can enter a mix of text, voice, and images into their prompts.

At the same time, ChatGPT can respond to users in up to five different AI-generated voices. This means that users can engage the chatbot in conversations via voice (although voice is limited to the ChatGPT app for Android and iOS). 

ChatGPT 3 vs ChatGPT4

Users also have the option to generate images directly within ChatGPT through the use of DALLE-3. 

Given that ChatGPT boasted 100 million weekly active users as of November 2023, the GPT-4V variant is one of the biggest multimodal AI tools on the market. 

3. Inworld AI

Inworld AI is a character engine that developers can use to create non-playable characters (NPCs) and virtual people. The solution enables developers to use LLms to develop characters to populate digital worlds and metaverse environments. 

One of the most notable aspects of Inworld AI is that its use of multimodal AI means that NPCs can communicate via a range of mediums, including natural language, voice, animations, and emotion. 

InWorld AI

Through the use of multimodal AI, developers can create smart NPCs. These NPCs not only have the ability to act autonomously but also have their own personalities and will express emotion to users based on certain trigger conditions. They also have their own memories of past events. 

Inworld AI is thus an excellent multimodal tool for those who want to use LLMs to build immersive digital experiences. 

2. Meta ImageBind

Meta ImageBind is an open-source multimodal AI model that can process text audio, visual, movement, thermal, and depth data, and Meta claims that this is the first AI model capable of combining information across six different modalities. 

For one example, feed ImageBind audio of a car engine and an image or prompt of a beach, and it will combine the two into new art.

Meta ImageBind The model itself can be used for diverse tasks, such as creating images from audio clips, searching for multimodal content via text, audio, and image, and giving machines the ability to understand multiple modalities. 

Meta said in the announcement blog post:

“ImageBind equips machines with a holistic understanding that connects objects in a photo with how they will sound, their 3D shape, how warm or cold they are, and how they move.”

This multimodal AI model has many uses but is most notable for its ability to enable machines to perceive their environments through sensors. 

1. Runway Gen-2

Runway Gen-2 is a multimodal AI model that can generate videos with text, image, or video input. Gen-2 enables the user to use text-to-video, image-to-video, and video-to-video to create original video content. 

Users also have the option to replicate the style of an existing image or prompt in the form of a video. This means that if there is an existing design that a user likes, they can mimic that compositional style in a new piece of content.

Gen-2 also provides users with the ability to edit video content. For example, with a text prompt, the user can isolate and modify subjects within the video. It can also be customized to deliver a higher fidelity result. 

So, if you’re looking for a solution to start creating videos from scratch, Gen-2’s multimodal approach to generative AI provides more than enough versatility to begin experimenting. 

The Bottom Line

The future of AI is multimodal and interoperable.

The more inputs a vendor supports, the more potential use cases there are for end users, and the more combinations of ideas are available in one spot for you.

If you want to experiment with multimodality in your workflow, we recommend using more accessible tools like ChatGPT or Runway Gen-2.

But it is a changing environment — we are still in the early days here. We will update you as more models come online with new features and ways of working.

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Tim Keary

Since January 2017, Tim Keary has been a freelance technology writer and reporter, covering enterprise technology and information security.