LLM vs Multimodal: Choosing the Right Foundation for AI Success

Your phone unlocks with your face. Your car reads road signs. Your email drafts itself before you finish typing. Behind these seamless experiences lies a fundamental question: how does AI truly "understand" what it sees, hears, or reads?

The answer depends on the intelligence architecture powering these systems.

Large language models (LLMs) function as text specialists, processing thousands of documents to generate precise outputs from textual data alone. However, when tasked with interpreting images or nuanced communication, they reach their limitations. This is where multimodal models demonstrate their value - AI systems designed to perceive information across multiple channels, similar to human cognition.

The business implications are substantial. McKinsey research indicates that organizations implementing generative AI across multiple modalities stand to gain up to USD 4.4 trillion annually through enhanced productivity.(Source: McKinsey)

For decision-makers, the strategic question has shifted from whether to implement AI to which model architecture will deliver the most relevant insights for specific business objectives.

This article examines the differences between LLMs and multimodal models, their practical applications, and how to select the optimal approach for measurable business outcomes. If you're evaluating LLM vs multimodal architectures for your enterprise, this guide will help clarify which is right for your use case.

Enterprise AI partners like Tredence are already helping businesses evaluate these models based on use case alignment, data maturity, and deployment readiness.

To begin with, let's explore the fundamental characteristics that define these AI architectures and how they process different types of information.

Understanding LLM & Multimodal Landscapes  

Selecting the optimal AI architecture requires a clear understanding of how each model type processes information. In the LLM vs multimodal conversation, success depends on recognizing the distinct data capabilities and functional strengths of each. Strategic implementation decisions hinge on these nuances.

LLMs: Specialists in Language  

Large Language Models (LLMs) are trained exclusively on text. They excel at reading and generating human-like language, identifying sentiment, summarizing content, answering queries, and drafting documents. Their strength lies in processing language at scale, making them invaluable for tasks rooted in text.

Consider a product review stating, "This chair looked great online, but what I received didn't match the picture." An LLM can analyze this sentence, detect customer dissatisfaction, extract entities like "chair" and "picture," and classify the issue as a quality or fulfillment problem. It understands the sentiment and complaint through textual analysis alone.

Multimodal Models: Readers, Watchers, and Listeners  

Multimodal models interpret multiple forms of data including text, images, audio, video, and more. They understand relationships between different inputs, making them suitable for use cases where context spans visual, auditory, or cross-channel information.

In the same product review scenario, if the customer uploads a photo of the chair they received, a multimodal model can compare it with the product listing image. It might identify color discrepancies, design differences, or missing features. This allows the system to validate complaints with visual evidence, something an LLM alone cannot accomplish.

Where LLMs interpret textual nuances, multimodal models also capture visual and auditory context that might otherwise be missed.

Having examined the distinct capabilities of each model type, let's compare their key differences to better understand when each delivers optimal value.

This distinction often forms the starting point in Tredence’s AI diagnostic workshops, where cross-functional teams evaluate enterprise data types, use case fit, and operational maturity before recommending a model architecture.

Difference Between LLM & Multimodal  

LLMs and multimodal models diverge significantly in their core capabilities despite sharing foundational AI architecture. These differences drive their suitability for specific business applications and determine their implementation requirements.

When comparing LLM vs multimodal capabilities, it’s clear that LLMs excel in language-intensive tasks, while multimodal models shine when the context requires multiple sensory inputs such as visuals, speech, and structured data.

Consider this real-world scenario involving customer service for a home decor brand. A customer submits feedback: "The rug I got doesn't match the image online—looks much duller in person."

An LLM processes this complaint, identifies customer dissatisfaction, and categorizes it as a potential fulfillment or quality issue. It can generate a response email and recommend ticket escalation based on textual analysis.

A multimodal model extends this capability. It evaluates the customer's uploaded photo against the original product image, confirming differences in color or pattern. This visual validation enables faster resolution pathways or automated refund approvals that text-only models cannot facilitate.

This comparison highlights the operational distinction: LLMs comprehend textual information, while multimodal models understand both textual and visual elements for more comprehensive analysis. Tredence builds on these distinctions to accelerate implementation. They help clients fine-tune models, deploy them into live workflows, and monitor performance with built-in governance from day one.

Moving from theory to practice, let's examine how organizations are implementing these technologies to solve real business challenges today.

Applications of LLM & Multimodals Right Now  

Enterprise adoption of LLMs and multimodal models has accelerated from theoretical potential to delivering quantifiable business impact. Tredence has helped organizations across telecom, legal, software, and healthcare operationalize these models, moving from experimentation to measurable outcomes. Organizations across sectors are embedding these technologies into critical workflows, achieving significant operational efficiencies and unlocking new capabilities.

Applications of LLMs  

Customer Support Automation  

A major telecom provider receives thousands of daily queries ranging from billing issues to service disruptions. Before implementing AI, support agents were overwhelmed, and resolution times were slow.

Now, a fine-tuned LLM handles the first layer of interaction. When a customer writes, "My internet has been down since last night and restarting didn't help," the model immediately:

• Classifies the issue
• Checks for known outages in the region
• Replies with an update or next step
• Creates a pre-filled support ticket with relevant details if escalation is needed

Such an implementation would significantly reduce the need for human intervention and improve average resolution times.

This kind of automation reflects what Tredence delivered for a Fortune 100 technology firm. By building an AI-powered communication suite across five platforms, Tredence enabled personalized, automated customer interactions that reduced human dependency and improved campaign performance by 30%.

Document Summarization and Legal Research  

At a global law firm, attorneys previously spent hours reviewing contracts to flag non-standard clauses or summarize key obligations. Today, they upload extensive documents into an LLM-powered tool.

For instance, a 50-page vendor agreement is condensed into a two-page brief highlighting:

• Termination terms
• Indemnities
• Payment obligations

The system also alerts lawyers to potential risks based on prior case data and standard policy benchmarks. This type of advancement would substantially reduce legal research time, allowing senior partners to focus on higher-value client work.

Code Generation and Software Development  

A product team building a customer onboarding portal leverages an LLM-enabled assistant (similar to GitHub Copilot) rather than starting from scratch.

Developers type a comment: "Build a React component for a multi-step signup form with email verification." The model suggests complete, functional code in seconds, including:

• Test cases
• Form validation logic
• Inline documentation

Developers can validate and customize as needed, potentially resulting in productivity improvements and shorter release cycles.

Applications of Multimodal Models  

Healthcare Diagnostics  

In hospital settings, radiologists receive chest X-rays alongside unstructured patient notes mentioning medical history and symptoms, plus structured lab results showing key metrics.

A multimodal model processes all inputs simultaneously. It can detect subtle abnormalities on X-rays, link them with patient symptoms and lab data, and flag potential diagnoses with high confidence.

Stanford's CheXNet model demonstrated that this multimodal approach can outperform experienced radiologists in certain diagnostic tasks, accelerating treatment decisions and improving patient outcomes.

Retail Product Discovery  

A customer redecorating their living room uploads a photo of a rust-colored velvet sofa into a furniture brand's app. Instead of endless scrolling, they receive curated recommendations for complementary items that match the sofa's color, texture, and style.

Behind the scenes, a multimodal model:

• Analyzes the uploaded image
• Cross-references product catalogs
• Incorporates text-based reviews
• Factors in product availability

Platforms like IKEA and Pinterest utilize similar technology to drive visual search and increase conversion rates by transforming product discovery into an intuitive, conversation-like experience.

A similar approach was implemented by Tredence for a global retailer. They built a unified customer data platform integrating over 70 data sources—including behavioral data, structured profiles, and purchase history—allowing for real-time personalization in product discovery. The platform drove a 14% increase in customer visibility and delivered $4.8 million in annual cost savings.

Autonomous Vehicles  

A self-driving car approaches an intersection while its multiple sensors collect diverse data:

• Cameras detect pedestrians and traffic signals
• GPS confirms exact location
• Radar senses nearby vehicle movements
• Audio systems parse voice commands

A multimodal model ingests this data in real time to make split-second decisions, such as slowing down and adjusting position to avoid potential hazards.

This capability to interpret multiple data sources simultaneously makes autonomous navigation increasingly reliable and responsive. Leading companies in this space rely heavily on multimodal AI to ensure safe decision-making in complex environments.

Examples of LLM & Multimodal  

Global enterprises are realizing substantial competitive advantages through strategic AI implementation. The following LLM vs multimodal examples highlight how industry leaders are applying each model type to solve distinct challenges, depending on their data formats, workflows, and business goals.

LLM Examples  

1. ChatGPT at Morgan Stanley  

Technology: GPT-4-based Large Language Model integrated via OpenAI's enterprise APIs

Problem: Morgan Stanley's wealth management division possessed over 100,000 internal research documents that advisors struggled to access efficiently.

Solution: The firm implemented a GPT-4-powered assistant trained on its internal content, allowing advisors to ask natural-language questions like "What are the key risks outlined in the latest emerging markets outlook?" and receive immediate, compliant answers.

Business Impact: The assistant streamlined how advisors accessed institutional knowledge, improving speed-to-insight and enabling more personalized client service without navigating through siloed resources. (Source: OpenAI, Morgan Stanley Use Case)

2. Legal Chatbot at PwC  

Technology: GPT-4 model fine-tuned for legal and professional services

Problem: PwC professionals invested significant time interpreting complex regulations, compliance documents, and policy updates for clients across regions.

Solution: PwC developed a ChatGPT-based tool to help legal and advisory teams ask compliance-related questions in natural language and receive summarized, legally accurate responses grounded in internal knowledge and external legal databases.

Business Impact: The tool accelerated routine legal interpretation and reduced dependence on senior consultants for first-level queries, allowing them to focus on more strategic work. (Source: PwC + OpenAI)

3. GitHub Copilot by Microsoft  

Technology: Codex LLM (a descendant of GPT-3) powering GitHub Copilot

Problem: Developers often dedicate excessive time to writing repetitive code, creating unit tests, and documenting functions.

Solution: GitHub Copilot, powered by OpenAI's Codex, assists developers by automatically generating code snippets, test cases, and inline documentation based on natural language prompts typed within the IDE.

Business Impact: According to GitHub, 55 percent of developers using Copilot reported faster coding, and over 75 percent felt more focused on solving complex problems rather than handling boilerplate code. (Source: GitHub Copilot Research)

Multimodal Examples  

1. Google Gemini in Enterprise R&D  

Technology: Google DeepMind's Gemini multimodal model

Problem: Enterprise teams often deal with fragmented inputs across formats, including charts, documents, screenshots, videos, making it difficult to synthesize insights from diverse sources.

Solution: Gemini 1.5, Google’s most advanced multimodal model to date, enables users to upload a mix of formats, such as a product chart, a snippet of source code, and a product feedback video, and ask a unified question like “What’s the root cause of declining user engagement in the last release?” The model analyzes the visuals, text, and metadata to generate a contextual answer.

Business Impact: While specific enterprise deployments are still emerging, Gemini's ability to handle 1 million tokens across multiple modalities sets a new benchmark in cross-format reasoning. Companies piloting Gemini report accelerated product diagnostics, reduced research effort, and faster time-to-insight. (Source: Google DeepMind Gemini Announcement)

2. Sephora's Virtual Artist  

Technology: Multimodal AI combining computer vision and NLP

Problem: Beauty shoppers often hesitate to purchase makeup online due to uncertainty about how products will appear on their skin tone or face shape.

Solution: Sephora launched the "Virtual Artist," powered by ModiFace (acquired by L'Oréal), which allows users to upload selfies and virtually try on products. It analyzes facial structure (image), understands user preferences (text), and recommends products accordingly.

Business Impact: In the first year, Sephora's AI-powered assistant facilitated more than 332,000 conversations across Singapore and Malaysia, resulting in an average monthly revenue uplift of $30,000. (Source: etailasia)

3. Amazon's Just Walk Out Technology  

Technology: Multimodal AI combining computer vision, sensor fusion, and real-time inference

Problem: Long checkout lines and friction in-store diminish customer experience and increase operational overhead.

Solution: Amazon deployed its Just Walk Out technology in Whole Foods and Amazon Go stores. The system uses ceiling-mounted cameras (vision), shelf sensors (structured data), and customer entry logs (textual and biometric inputs) to detect what shoppers select and charge them automatically as they exit.

Business Impact: The system is now operational in over 100 locations, reducing checkout friction and labor costs while enhancing convenience and customer satisfaction. (Source: Amazon Just Walk Out)

With a clear understanding of these models' capabilities and applications, how can businesses determine the right approach for their specific needs?

From Understanding to Impact: Choosing the Right AI Foundation with Tredence  

The LLM vs multimodal decision is more than a technical comparison—it represents a strategic inflection point for enterprise AI adoption. Each architecture offers distinct advantages that align with specific organizational objectives and operational contexts.

Multimodal models excel when AI needs to process information through multiple channels simultaneously, integrating visuals, speech, text, and structured data into comprehensive insights.

As enterprises advance their AI implementation strategies, one of the most pressing choices is LLM vs multimodal—a decision that hinges less on hype and more on alignment with business objectives, data maturity, and operational readiness.

This is where Tredence delivers significant value.

With a proven track record in implementing enterprise-grade AI solutions, Tredence guides organizations from initial exploration to successful execution. Whether your priority is operational automation, decision intelligence, or enhanced customer experience, Tredence builds systems designed for scale and measurable return on investment.

What Tredence brings to the table:  

• End-to-end LLM and multimodal strategy development
• Model selection, fine-tuning, and deployment tailored to your data ecosystem
• Domain-specific accelerators for faster time-to-value
• Integration of AI models into existing enterprise workflows
• AI governance, bias mitigation, and model monitoring at scale
• Cross-functional teams combining data science, engineering, and domain expertise

Contact Tredence today to discover which AI foundation, LLM or multimodal, can transform your data into your next competitive advantage.

FAQs  

1. Do multimodal LLMs require more training data than standard LLMs?  

Yes, multimodal LLMs require significantly more training data than standard LLMs. They need diverse datasets spanning images, audio, video, and text to learn cross-modal relationships, while text-only LLMs only need text corpora. Organizations implementing multimodal models should prepare for more extensive data collection and storage requirements.

2. Can a traditional LLM be upgraded into a multimodal model?  

Traditional LLMs cannot be directly upgraded into multimodal models. Converting requires architectural modifications, additional training on diverse data types, and new components for processing non-textual inputs. Most successful multimodal models are designed with multimodality as a core principle from inception, requiring new implementation rather than retrofitting.

3. How do multimodal LLMs impact AI-generated content compared to LLMs?  

Multimodal LLMs transform AI-generated content by enabling richer outputs that incorporate visual and audio elements alongside text. Unlike standard LLMs that produce text-only content, multimodal models generate content informed by images and interpret visual nuance. This enables more engaging customer experiences and intuitive interfaces that respond to multiple input types simultaneously.

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Editorial Team
Tredence