What is Multimodal AI? Full Guide to Large Multimodal Models

Q: What challenges arise when training and deploying large multimodal models, and how can they be overcome?

Training and deploying large multimodal models (LMMs) presents several challenges, particularly in areas like data quality, computational requirements, and model complexity. A critical hurdle is developing datasets that effectively align different types of data, such as images paired with text descriptions. Misaligned data can lead to reduced accuracy, especially when the model encounters new tasks. To tackle this, researchers rely on methods like advanced filtering, semi-supervised learning, and leveraging large-scale web data. These approaches help improve dataset quality without the need for exhaustive manual labeling. Another significant challenge lies in the high computational demands of training and deploying LMMs. These models require extensive GPU resources and sophisticated architectures to handle diverse data formats. To address this, techniques such as sparse attention mechanisms, model quantization, and pruning are employed. These methods optimize performance, reduce latency, and lower resource usage, ensuring that LMMs remain efficient and practical for real-world applications.

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What is Multimodal AI? Full Guide to Large Multimodal Models

Multimodal AI combines text, images, audio, and video into one system, making AI smarter and more efficient. It’s transforming industries by enabling tasks like analyzing medical images with patient data or generating code from design mockups. By 2027, 40% of AI tools will be multimodal, up from just 1% in 2023, with the market reaching $10.89 billion by 2030.

Key Takeaways:

What it does: Processes multiple data types (text, images, audio, video) together for better insights.
Why it matters: Reduces inefficiencies and enhances accuracy in complex tasks.
Top models: Google Gemini, OpenAI GPT-4o, and Anthropic Claude 3, each excelling in different areas.
Industries impacted: Healthcare, e-commerce, automotive, and more.

Want to automate workflows with multimodal AI? Tools like Latenode let you integrate advanced models like Gemini and GPT-4 into your processes without coding.

Multimodal AI: LLMs that can see (and hear)

What is Multimodal AI?

Multimodal AI brings together various types of data - like images, sounds, and text - into a unified system, mimicking the way humans process information. By integrating these data types, it achieves a much deeper understanding of context than traditional AI systems. In 2023, the global market for multimodal AI was valued at $1.2 billion, with projections showing an annual growth rate of over 30% from 2024 to 2032 ^[2]. This rapid growth signals the increasing importance of multimodal AI in streamlining and automating business workflows.

Understanding Modalities in AI

In AI, a "modality" refers to a specific type of data input that a system can process ^[3]. Each modality - whether visual, auditory, or textual - provides unique information, and when combined, they offer a richer, more complete picture.

The advantages of combining modalities are already visible in practical applications. For instance, Uniphore’s conversational AI platform improves call center efficiency by simultaneously analyzing voice tone, facial expressions, and text ^[2]. As Abby Curtis and Chrissy Kidd from Splunk Blogs put it:

"Multimodal AI can handle multiple data inputs (modalities), resulting in a more accurate output" ^[3]

By integrating multiple data types, these systems reduce the ambiguities often seen in single-modality approaches, providing a more nuanced understanding of context.

Aspect	Single-Modality AI	Multimodal AI
Data Handling	Processes one data type only	Handles multiple data types simultaneously
Integrated Analysis	Limited to specialized tasks	Excels in complex, multi-layered scenarios
Training Speed	Quick to train and deploy	Requires diverse datasets and more resources
Use Cases	Text generation, speech-to-text	Image captioning, cross-modal understanding
Contextual Understanding	Limited by single input	Combines inputs for richer context

These strengths highlight how multimodal AI systems, particularly Large Multimodal Models (LMMs), are reshaping AI by moving from isolated tasks to integrated, holistic analysis.

The Evolution from LLMs to LMMs

Large Multimodal Models (LMMs) take the capabilities of Large Language Models (LLMs) to the next level by incorporating visual and auditory data alongside text. While LLMs excel at text-based tasks, LMMs extend functionality to include images, videos, and audio, making them far more versatile ^[5]. This advancement brings AI closer to understanding and interacting with the world in a way that feels more human-like.

LMMs achieve this by using large-scale datasets and advanced neural network architectures to identify patterns across different data types ^[5]. For example, they can generate captions for images or answer questions that require combining visual and textual insights. In September 2024, Meta AI introduced LlaMA 3.2, an open-source LMM capable of processing text and visual data simultaneously, improving both user interaction and content accuracy ^[4].

A key feature of LMMs is their use of local and global attention mechanisms. These systems focus on specific image regions that align with corresponding text (local attention) while also integrating broader semantic information across the entire input (global attention) ^[5]. This dual focus allows for greater precision and adaptability, making LMMs effective in complex scenarios, such as interpreting medical data for healthcare or analyzing financial trends ^[5]. By bridging these modalities, LMMs pave the way for more natural and effective human-AI interactions.

Popular Multimodal AI Models in 2025

As multimodal AI continues to evolve, 2025 has brought a wave of advanced models that seamlessly process text, images, audio, and video together. Unlike earlier systems, which required separate models for different data types, these new systems integrate multiple modalities natively. Below, we explore some of the most influential models shaping this landscape and their standout features.

Top LMMs Available Today

Several leading multimodal models dominate the AI scene in 2025, including Google Gemini, OpenAI GPT-4o, and Anthropic Claude 3. Each model brings unique strengths to the table, redefining how businesses handle and integrate diverse data.

Google Gemini: Recognized as the most versatile multimodal system, Gemini supports text, images, audio, and video processing natively. Its Gemini 2.5 Pro version boasts a staggering 1 million token context window, enabling it to handle extensive content like entire books or lengthy video transcripts in one go. Notably, Samsung integrated Gemini into its Galaxy S25 series in early 2025, highlighting its practical applications ^[6]^[7].
OpenAI GPT-4o: Known for its precision in visual analysis, GPT-4o handles tasks involving both text and images, such as analyzing photos, screenshots, and scanned documents. Its upgraded version, GPT-4.5, supports up to 128,000 tokens, expanding its capacity for complex tasks like interpreting graphs or combining visual and textual data ^[6]^[7].
Anthropic Claude 3: Designed for conversational ease, Claude 3 excels in interpreting images and text in a more interactive manner. The Claude 3.5 update introduces a 200,000 token context window, making it ideal for analyzing large projects, whether single documents or extensive codebases ^[7].

Model	Context Window	Modalities Supported	Key Strength
Gemini 2.5 Pro	1 million tokens	Text, images, audio, video	Comprehensive multimodal processing
GPT-4.5	128,000 tokens	Text, images	High accuracy in visual analysis
Claude 3.5	200,000 tokens	Text, images	Conversational image interpretation

Native Multimodal Capabilities

What sets these models apart is their ability to process multiple data types natively, without requiring conversion between formats. This capability enables them to handle complex tasks more efficiently and provide richer insights. For example, Google Gemini can analyze a business presentation containing charts, spoken narration, and written notes, synthesizing all elements into a cohesive understanding ^[7].

Native multimodal processing is particularly valuable in scenarios requiring a deep understanding of relationships between different data types. For instance, when analyzing a document that combines text with images, these models interpret both formats directly, eliminating the need for intermediate steps like converting images into text. This approach streamlines workflows and enhances the depth of insights across industries.

Practical Applications Across Industries

The adoption of multimodal AI is expected to reach 40% by 2027 ^[6], driven by its transformative applications:

Healthcare: Multimodal AI systems analyze medical images like X-rays and MRIs alongside patient histories, identifying early signs of illness. By cross-referencing pathology reports and genetic data, these models offer precise treatment recommendations ^[8].
E-commerce: Platforms leverage multimodal AI to evaluate customer reviews and product images together. This enables them to identify popular features and align product recommendations with user browsing behavior and visual preferences ^[8].

How Do Large Multimodal Models Work?

Large multimodal models are designed to process and understand multiple forms of data - such as text, images, audio, and video - simultaneously. They rely on transformer architectures, which excel at handling sequences of interconnected information. Unlike traditional models that focus on isolated data points, transformers analyze relationships within and across data types, making them ideal for integrating diverse inputs ^[9]. This foundational technology is what enables these models to bridge different modalities effectively.

Transformer Architecture: The Foundation of LMMs

At the heart of large multimodal models (LMMs) lies the transformer architecture, which employs self-attention mechanisms to identify relationships within and between data types. This allows the model to merge information from various sources into a cohesive understanding ^[11].

Here’s how it works: each data type - whether it's an image, a piece of text, or audio - is processed through its own specialized encoder. These encoders convert the inputs into vector representations, known as embeddings. For instance, if you input an image and a descriptive text, the model creates separate embeddings for each. These embeddings are then combined into a unified input sequence, often enhanced with positional encodings to retain spatial or temporal context ^[11].

Through self-attention and cross-attention mechanisms, the model identifies patterns and relationships across modalities. For example, it might connect a chart's visual details with its accompanying textual explanation ^[9].

Recent advancements, like the Mixture-of-Transformers (MoT), have further refined this process. MoT separates modality-specific parameters, which reduces computational demands while preserving global self-attention capabilities. Tests with the Chameleon model demonstrated that MoT could achieve comparable performance using only 55.8% of the FLOPs, and even as little as 37.2% when incorporating speech as a third modality ^[10].

Training and Fine-Tuning

Training large multimodal models involves several intricate steps. First, raw data is converted into embeddings using specialized encoders. These embeddings are then fused into a single representation. The model’s parameters are adjusted to minimize the gap between its predictions and the actual data, enabling it to learn effectively ^[12].

Fine-tuning is a particularly important phase, where the model learns how different modalities relate to one another. For example, it might learn to associate spoken words with corresponding visual scenes or align textual descriptions with image content. This process relies on carefully curated datasets to ensure accuracy ^[12].

A key method for fine-tuning is Reinforcement Learning with Human Feedback (RLHF). This approach uses human ratings to guide the model toward generating outputs that are both accurate and safe. RLHF involves four stages: data collection, supervised fine-tuning, creating a reward model, and optimization. These steps help improve the model's reliability and reduce harmful outputs ^[14]^[16]. For instance, OpenAI found that labelers preferred outputs from a 1.3 billion-parameter version of InstructGPT over those from the much larger 175 billion-parameter GPT-3. Additionally, studies with GPT-4 showed that RLHF doubled the model’s accuracy on challenging questions ^[15].

Although training LMMs requires substantial computational resources and expertise, ongoing improvements in architecture and training techniques make the effort worthwhile. These advancements enable multimodal models to excel in a wide range of practical applications, from content generation to complex business automation ^[12]^[13].

What Can a Large Multimodal Model Do?

Large multimodal models (LMMs) are transforming industries by automating workflows and offering innovative solutions that combine visual, textual, and audio data. These models excel at processing and generating content across multiple formats, paving the way for applications in image analysis, code generation, and voice interaction. The global multimodal AI market is estimated to grow to $10.89 billion by 2030 ^[17]. This growth highlights the increasing demand for systems that integrate diverse data types to address complex challenges.

Image Description and Analysis

LMMs are highly capable of analyzing images, charts, and other visuals to extract meaningful insights. By using advanced encoders, these models convert visual information into vector formats, allowing them to process it alongside text and other data types. This capability is applied across various industries:

Retail: Online platforms leverage LMMs to generate image descriptions for products like groceries and meals, reducing the need for manual input ^[18].
Manufacturing: By merging visual inspection data with production details, LMMs help identify and prevent defects before they occur ^[18].
Healthcare: Multimodal analysis enables healthcare providers to correlate imaging data with patient demographics and treatment protocols, improving patient outcomes ^[18].

In the auto insurance sector, LMMs analyze vehicle damage images, pinpoint specific issues, and estimate repair costs, streamlining claims processing ^[13]. Similarly, in healthcare, these models combine textual descriptions of symptoms with medical imaging to assist in diagnosis. For instance, IBM Watson Health integrates data from electronic health records, clinical notes, and imaging to enhance disease diagnosis and personalize treatments ^[17].

Code Generation from Mockups

LMMs are also reshaping software development by converting design mockups and wireframes into functional code. This capability bridges the gap between design and development, significantly reducing the time needed for prototyping. By analyzing elements like layouts, buttons, and color schemes, LMMs generate code in formats such as HTML, CSS, JavaScript, and mobile app frameworks. This approach minimizes manual coding, making it particularly useful for creating responsive web designs.

This functionality not only speeds up the design-to-code process but also enhances productivity, allowing developers to focus on refining user experiences rather than starting from scratch.

Voice Interaction and Audio Analysis

LMMs are equally adept at handling audio data, offering capabilities like speech transcription, emotional tone analysis, and text-to-audio conversion. These features are being utilized in various sectors:

Automotive: Companies like 704 Apps use LMMs to analyze conversations in vehicles. For example, Gemini monitors emotional "temperature" by identifying words like "robbery" or "assault" and triggers alerts to preempt potential risks ^[19]. Volkswagen of America employs LMMs in its myVW app, allowing drivers to use voice commands to explore owners' manuals or identify dashboard indicators using their smartphone cameras ^[19].
Retail: LMMs power seamless self-checkout systems by combining voice commands, visual recognition, and payment processing ^[13].

When integrated with platforms like Latenode, these voice interaction capabilities become even more powerful. Businesses can create automated workflows that react to audio inputs, triggering actions across various applications. For instance, a retail store could use Latenode to process a customer’s voice command to check product availability and automatically send follow-up notifications or updates.

LMMs are redefining how businesses operate, offering practical solutions that save time, enhance accuracy, and improve user experiences across industries.

Automate Your Multimodal AI Models with Latenode

Latenode

Latenode takes the potential of multimodal AI and seamlessly integrates it into everyday business operations. While multimodal models like GPT-4 or Gemini excel at advanced data analysis, their real power emerges when they are embedded into workflows. Latenode simplifies this process, turning complex AI capabilities into automated systems that operate effortlessly across your tech stack.

Connecting LMMs via APIs

Managing multiple AI subscriptions can be overwhelming, but Latenode eliminates this hassle by centralizing access to over 400 AI models ^[20]. This includes leading large multimodal models (LMMs) such as OpenAI's GPT-4, Google's Gemini, and Anthropic's Claude. With its visual workflow builder, Latenode allows users to connect these models to their business applications without needing to write code. For those who prefer customization, JavaScript-based tweaks are fully supported.

"AI Nodes are amazing. You can use it without having API keys, it uses Latenode credit to call the AI models which makes it super easy to use. - Latenode custom GPT is very helpful especially with node configuration" - Islam B., CEO Computer Software ^[20]

This streamlined approach significantly reduces the technical complexity of AI integration. Teams no longer need to juggle different vendor accounts, monitor various usage limits, or manage separate authentication systems. By simplifying these connections, Latenode enables businesses to focus on creating impactful, automated workflows.

Latenode Workflow Examples

SEO Content Automation with Gemini 2.5 Pro: Anastasia Antonova, a founder at Latenode, designed an automated workflow that increased organic traffic by 38% in just one month. The process identifies trending topics, extracts content using news APIs and headless browsers, employs Gemini 2.5 Pro to analyze SEO keywords, and then generates fully optimized articles. Each article costs between $0.40 and $0.60 to produce and takes only 10 minutes to create. Impressively, these articles started ranking on Google's second page shortly after publication ^[20].

Latenode’s capabilities extend beyond content creation:

Product Description Generation: Retailers can link product image uploads to ChatGPT through Latenode. When new images are added to a content management system, the workflow automatically generates detailed descriptions, identifies key features, and updates the product database.
Voice-to-Content Workflows: Using Latenode's Speech-to-Post template, voice notes are transformed into polished social media posts. This process combines ChatGPT with tools like Recraft for generating accompanying visuals.

"The AI javascript code generator node is a life saver, if you get to a point in the automation that a tool or node is not yet created to interact with Latenode, the AI…" - Francisco de Paula S., Web Developer Market Research ^[20]

These examples highlight how Latenode bridges the gap between cutting-edge multimodal AI and practical business automation. By embedding advanced AI into workflows, companies can transform innovative technology into tangible results, enhancing efficiency and productivity.

Conclusion: The Future of Multimodal AI

Multimodal AI is transforming how businesses operate, signaling a major shift in enterprise technology. The global multimodal AI market is expected to reach $10.89 billion by 2030 ^[17], and Gartner estimates that by 2027, 40% of generative AI solutions will incorporate multimodal capabilities, a dramatic rise from just 1% in 2023 ^[1]. These numbers highlight the rapid adoption of this technology and its growing importance across industries.

Leading companies are already harnessing multimodal AI to achieve groundbreaking results. For instance, Amazon uses a packaging optimization system that combines product dimensions, shipping needs, and inventory data to reduce waste while aligning with sustainability goals. Walmart employs shelf cameras, RFID tags, and transaction data to refine inventory management and improve demand forecasting. Similarly, JP Morgan’s DocLLM processes textual data, metadata, and contextual information from financial documents, enhancing risk evaluation and compliance efforts (Source: Appinventiv, May 2025).

"Multimodal AI can tackle more complex challenges, create more personalized experiences, and help companies adapt more effectively. It's about versatility and deeper insights, which are crucial to staying ahead", says Scott Likens, US and global chief AI engineering officer at PwC ^[21]. Arun Chandrasekaran, distinguished VP and analyst for artificial intelligence at Gartner, adds, "It enables use cases that previously weren't possible" ^[21].

By integrating voice, images, text, and structured data, multimodal AI opens the door to innovations that deliver measurable business value. When these capabilities are embedded into automated workflows, platforms like Latenode make them even more powerful.

Latenode simplifies access to top multimodal models like GPT-4, Gemini, and Claude, streamlining integration and automation. Whether it’s creating SEO content, generating image-based product descriptions, or enabling voice-driven communication, Latenode enables businesses to seamlessly embed multimodal AI into their operations. This approach not only enhances efficiency but also sets the foundation for sustained competitive advantage.

As multimodal AI evolves, organizations that prioritize integration platforms will position themselves as leaders in their fields. The future belongs to those who can orchestrate these advanced capabilities effectively - and Latenode is here to make that future a reality today.

FAQs

How does multimodal AI make complex tasks more accurate and efficient compared to single-modality AI?

Multimodal AI transforms how complex tasks are handled by merging different types of data - such as text, images, audio, and video - into a unified system. This combination provides a deeper understanding of context, minimizing the likelihood of errors that can arise when relying solely on one type of input.

By analyzing diverse data simultaneously, multimodal AI uncovers patterns and relationships that single-modality systems might overlook. It also proves to be more robust against noise or inconsistencies in the data, making it a dependable choice for practical uses like image analysis, voice recognition, and tasks involving multiple data formats. For businesses, this means managing intricate workflows with higher accuracy and efficiency, which directly leads to improved productivity and better user experiences.

Which industries can benefit the most from large multimodal models, and how do they apply them?

Large multimodal models (LMMs) are reshaping various industries by combining different types of data - such as text, images, and audio - to provide advanced insights and enable automation.

In healthcare, LMMs bring a new level of precision by analyzing medical images, patient histories, and doctors' notes together. This integration supports more accurate diagnoses and speeds up treatment planning. E-commerce businesses leverage LMMs to create tailored shopping experiences, blending user behavior, product reviews, and visual data to offer personalized recommendations that boost engagement and drive sales. The automotive industry relies on LMMs for autonomous vehicles, where they process data from cameras, sensors, and maps to make real-time decisions on the road. For customer service, LMMs simplify support workflows by interpreting a mix of inputs, such as text and images, to resolve issues faster and improve customer satisfaction.

These applications demonstrate how LMMs are transforming workflows and creating meaningful advancements across multiple sectors.

What challenges arise when training and deploying large multimodal models, and how can they be overcome?

Training and deploying large multimodal models (LMMs) presents several challenges, particularly in areas like data quality, computational requirements, and model complexity. A critical hurdle is developing datasets that effectively align different types of data, such as images paired with text descriptions. Misaligned data can lead to reduced accuracy, especially when the model encounters new tasks. To tackle this, researchers rely on methods like advanced filtering, semi-supervised learning, and leveraging large-scale web data. These approaches help improve dataset quality without the need for exhaustive manual labeling.

Another significant challenge lies in the high computational demands of training and deploying LMMs. These models require extensive GPU resources and sophisticated architectures to handle diverse data formats. To address this, techniques such as sparse attention mechanisms, model quantization, and pruning are employed. These methods optimize performance, reduce latency, and lower resource usage, ensuring that LMMs remain efficient and practical for real-world applications.