Top 10 Multimodal LLMs to Explore in 2025 - Analytics Vidhya

Top 10 Disruptive Multimodal Large Language Models (MLLM) of 2025

Multimodal Large Language Models (MLLM) represents the pinnacle of artificial intelligence, which easily bridges the gap between different data patterns (text, images, audio and video). Unlike old models that only deal with text information, MLLM combines multiple patterns to provide richer and more contextual insights. This convergence of advantages has revolutionized industries, enabling everything from complex scientific research and automated customer support to innovative content creation and end-to-end data analysis.

In recent years, artificial intelligence has developed rapidly. Previous language models only support plain text, but have made significant progress in embedding visual, auditory and video data. Contemporary multimodal large language models set new records in performance and versatility, indicating that intelligent multimodal computing will become the standard in the future.

In this blog post, we will introduce the top ten top multimodal large language models that are changing the artificial intelligence ecosystem in 2025. These models are constructed by industry leaders such as OpenAI, Google DeepMind, Meta AI, Anthropic, xAI, DeepSeek, Alibaba, Baidu, ByteDance and Microsoft. They not only reflect the current status of artificial intelligence, but also point out the direction of future innovation. : : : : : : : : : : : : : : : It is good at operations such as in-depth reasoning, creative content generation and multimodal perception. It is built for enterprise-grade applications, is scalable and integrates seamlessly with Google Cloud solutions. Its advanced design enables it to handle complex processes, enabling it to be used in industries such as healthcare, entertainment and education.

Main functions

• Multimodal advanced functions (image, text, audio, video).
• High precision in complex reasoning and creative activities.
• Enterprise-level scalability.
• Seamlessly integrate with Google Cloud services.
How to use it?
Gemini 2.0 can be accessed through Google Cloud's Vertex AI platform. Developers can sign up for a Google Cloud account, enable the API and integrate it into their applications. Detailed documentation and tutorials are available on the Google Cloud Vertex AI page.

2. xAI's Grok 3

• Organization: xAI
• Knowledge deadline: February 2025
• Limit:Proprietary
• Parameters: Not disclosed

xAI's flagship multimodal large language model Grok 3 is designed for complex reasoning, complex problem solving and real-time data processing. It is able to accept text, image and audio inputs, allowing it to adapt to a variety of uses, including financial analysis, autonomous systems and real-time decision making. Thanks to Grok 3's efficiency and scalability optimization, high performance is guaranteed even with large data sets.

Main functions

• Real-time data processing and analysis.
• Multimodal inference (text, images, audio).
• High efficiency in processing large data sets.
• Designed for applications that require quick decisions.

How to use it?

Grok 3 can be accessed through xAI's official website. Developers need to register an account, obtain API credentials, and follow the integration guides provided on the xAI developer portal.

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Conclusion

Multimodal Large Language Model (MLLM) is rapidly growing in 2025, and it can handle text, images, audio and video. This enhances the user experience and expands the application of artificial intelligence in various industries. Key trends include the emergence of open source models, increased investment in artificial intelligence infrastructure, and the development of dedicated models for specific tasks. All of this together drives artificial intelligence into various industries and makes it a fundamental technology in modern technology.

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