LLM vs. GenAI: Key differences, examples, and uses explained
Do you know the difference between large language models and generative AI? Let’s break down what they are, how they work, and how businesses can leverage them.
Generative AI (gen AI) and Large Language Models (LLMs) have become household names but are often misunderstood. While they’re closely related, they’re not the same thing, and understanding the distinction matters. Whether you’re just comparing LLM vs generative AI, evaluating solutions for your business, or simply trying to keep up with the pace of innovation, knowing how these technologies differ will help you make better decisions.
This blog will guide you through the difference between LLMs and generative AI in a clear, approachable way. By the end, you’ll have a practical and jargon-free understanding of generative AI vs large language models and how both are shaping the future of intelligent systems.
Let’s start with this simple analogy to break the ice:
Imagine GenAI as an experienced chef, who can create a wide range of dishes from scratch. This chef has been trained in tons of cooking techniques and understands the fundamentals of flavor combinations. They can whip up entirely new recipes on a whim. They have cookbooks with images, a video series, and even voice instructions so you can follow along hands-free. Basically, GenAI is Gordon Ramsey.
On the other hand, LLMs are like incredibly vast libraries filled with cookbooks from all over the world. These cookbooks contain countless recipes, instructions, and tips collected from generations of culinary tradition. When you ask for a recipe, the LMM searches through its extensive collection to find the most suitable one based on your request, presenting it to you with all the necessary details in writing.
GenAI learns from LLMs to produce new and unique content. Just like how Gordan Ramsey was trained on hundreds of recipes and techniques from around the world, and that’s what inspires him to create new and unique dishes. See the connection?
In essence, while GenAI creates entirely new content based on its understanding of data, LLMs retrieve existing content from their data set based on your input. Both approaches serve the purpose of providing content based on user input, but they operate in fundamentally different ways.
Let’s dive a bit deeper into both technologies to better understand them.
What is generative AI?
Generative AI (GenAI) is a type of artificial intelligence that focuses on different types of content generation, not just analyzing data or answering questions. Generative AI systems can produce original:
- Text
- Images
- Music
- Video
- Code
- 3D objects
- Audio
It does this by learning patterns from very large datasets. After training, the model can produce newly generated content that looks or sounds like the data it was trained on.
Types of generative AI models (2025 overview)
Generative AI is broader than just text-based models. It includes several different approaches:
1. Generative Adversarial Networks (GANs)
GANs have two AI systems that compete with each other to create realistic images. For example, if both are trained on photos of clowns, they can generate a completely original image of a clown that doesn’t actually exist.
2. Diffusion models
Diffusion models are trained by taking an image, turning it into random noise (like you would see on a static TV), and then returning it to its original form. After enough training, the model learns how to clean noise. That essentially means it can start with noise and create what you ask it to.
Modern image and video generators, like DALL-E 4, Midjourney v7, and Runway’s Gen-3 Alpha use diffusion.
Visual representation of how a diffusion model turns an image into “noise”, then back to its original state for training.
3. Transformer Models
Transformers understand relationships between words or pixels. They power today’s most advanced AI systems, including LLMs and multimodal models like GPT-5.1, Claude 3.7 Sonnet, and Gemini 2.0 Ultra.
4. Neural Radiance Fields (NeRFs)
NeRFs can generate 3D content from multiple 2D images. Essentially, it can analyze images of the same item from different angles to produce an accurate 3D model of the image. This GenAI model can be useful in a number of fields such as architecture and robotics.
5. Variational Autoencoders (VAEs)
A VAE model learns how to take something big, like a picture, and shrink it into a small code, keeping only the most important information. Then, it can recreate the image from the code. It’s able to create a range of different codes so it becomes very good at creating something new.
Examples of Generative AI (2025)
- Midjourney: Text-to-image generator
- Sora: Text-to-video model capable of realistic cinematic footage
- Suno AI: Generate music from simple prompts
- 3D AI Studio: Create 3D models from text or 2D images
Key takeaway:
The key feature of generative AI is its ability to generate new and original content across multiple formats. Generative AI is a wide category of systems that can create many different types of content. Large Language Models (LLMs) are just one type of generative AI.
What are Large Language Models (LLM)?
A Large Language Model (LLM) is a type of generative AI focused specifically on understanding and generating human language, written or spoken.
During training, the LLM learns:
- how words relate
- how sentences flow
- what concepts mean
- how to interpret context
- how to follow instructions
LLMs use a technology called a transformer, which acts like a super-charged language brain. It pays attention to how all the words in a sentence (or paragraph) relate to one another.
LLMs handle text and language tasks such as:
- Text generation
- Writing and editing
- Summarizing
- Email drafting
- Translation
- Coding assistance
- Customer support
- Reasoning and analysis
- Conversational dialogue
Examples of LLMs (2025)
GPT-5.1 (OpenAI)
The newest generation of GPT, used in ChatGPT. Known for reasoning, writing, real-time processing, and multimodal inputs (text, image, audio, and now limited video).
Grok (xAI)
A model focused on accuracy, safety, and advanced reasoning. Popular among researchers and enterprises.
Meta Llama 4
A powerful open-source LLM widely used by developers. Strong in coding, instruction following, and custom fine-tuning.
Google Gemini 3 Pro
A multimodal model trained across text, vision, audio, and more. It has strong reasoning skills and large context handling. Multimodal LLMs In 2025, it’s almost standard for LLMs to interpret images, audio, and video in addition to text, also known as multimodal LLMs.
For example:
- You can upload a photo of a dish → the LLM identifies it
- You can send a video of meal prep → it writes step-by-step instructions
- You can voice note a list of ingredients → it suggests meals you can cook
These capabilities blur the lines between “language models” and “full generative AI,” but LLMs are still defined by their language-first foundation.
What’s the difference between LLMs and Gen AI?
So now we know LLMs and Gen AI are not the same thing. The easiest way to think about it is:
- Generative AI is the entire category.
- LLMs are one specific type within that category, focused on language.
Both generative AI and LLMs rely on deep learning techniques to identify patterns and relationships from vast amounts of datasets. But generative AI includes many types of models (image generators, audio models, video models, 3D models, etc.), while LLMs specialize in understanding and generating text.
Below is a simple breakdown of how they differ.
Large language model vs generative AI: Comparison table
| Category | Large Language Model (LLM) | Generative AI |
|---|---|---|
| Definition | A type of AI designed to understand and generate human language | A broad category of AI systems that create new content (text, images, audio, video, 3D) |
| Examples (2025) | GPT, Grok, Gemini, Llama | DALL-E, Midjourney, Sora, Suno AI, 3D AI Studio |
| Main purpose | Language understanding, writing, reasoning, coding | Content creation across multiple formats and modalities |
| Prompt type | Mostly text (many now support images/audio) | Text, images, audio, video, 3D data depending on the model |
| Output type | Text (plus images/video for multimodal LLMs) | Text, images, audio, video, 3D models, music, motion, more |
| Core technology | Type of neural network architecture called a transformer | Mix of transformers, diffusion models, GANs, VAEs, NeRFs |
| Training data | Text-heavy datasets (books, code, articles, conversations) | Can include text, images, audio, video, 3D scans, sensor data |
| Best for | Writing, summarization, coding, answering questions, analysis | Visual creation, video generation, sound/music, simulation, plus language |
| Scope | Narrower focused on language tasks | Much broader, covers all generative tasks |
| Relationship | A subset of generative AI | The umbrella category that includes LLMs |
Use cases: LLM vs generative AI
So, when should you use which? Here’s a quick break down of the best use cases for both LLMs and various generative AI tools:
LLM use cases:
LLMs read, write, summarize, analyze, and reason with text. Although newer models can understand multimodal prompts, their core functionality is still focused on language.
1. Content creation
- Blog posts
- Emails
- Reports
- Social media content
- Scripts and outlines
2. Summarization and research
- Long documents
- Meeting transcripts
- Articles and reports
- Scientific papers
3. Customer support and chatbots
- Automated responses
- Personalized support
- FAQ handling
- Troubleshooting guidance
4. Coding and developer assistance
- Explaining code
- Debugging
- Generating scripts
- Refactoring and documentation
5. Language translation and localization
- Multilingual communication
- Document translation
- Real-time interpretation
Generative AI use cases (2025)
Generative AI goes far beyond text. It includes models that produce images, videos, audio, 3D assets, design concepts, simulations, and more.
1. Image generation and design
- Branding and logo design
- Product mockups
- Marketing visuals
- Concept art
- UI/UX wireframes
2. Video generation
- Marketing videos
- Short films
- Animations
- Storyboards
- Training videos
3. Audio and music
- Songs
- Sound effects
- Background music
- Voiceovers
4. Simulations and digital twins
- Physical environments
- Product behavior
- Chatbot personalities
- Factory layouts
5. 3D asset generation
- Game assets
- Architecture models
- Visual environments
- Robotics simulations
Examples of how brands use LLMs:
Megi Health Platform
Megi Health uses an assistant on WhatsApp to make it easier for people with chronic conditions to stay on top of their health. Patients can quickly send updates like blood pressure readings or symptoms, learn more about their condition, and get connected to a doctor when needed, all through a chat interface they already know. The system helps doctors by collecting clear, consistent data ahead of time, and it saves medical staff a lot of manual work. Patients get faster support, feel more cared for, and overall have a smoother, more comfortable healthcare experience.
Hrvatski Telekom
Hrvatski Telekom uses Infobip’s chatbots on WhatsApp, Viber, and SMS to help customers get answers quickly without calling support. People can check their bills, manage payments, fix issues with POS devices, or get simple troubleshooting steps right from their phone. The chatbot handles most routine questions automatically, which reduces pressure on the call center and speeds up help for customers. It also sends gentle reminders that encourage people to pay their bills on time, which has improved collection rates. Overall, the system makes support faster, easier, and more efficient for both customers and the company.
Coolinarika by Podravka
Podravka’s Coolinarika platform uses an AI cooking assistant that helps users find recipes, plan meals, and learn more about ingredients in a fast, conversational way. The assistant can suggest dishes based on what someone has at home, offer healthier alternatives, explain nutritional details, and guide users step-by-step through cooking. It also personalizes suggestions so people can discover new meals that match their tastes or dietary needs. Overall, it turns everyday food questions into quick, helpful answers that make cooking easier, smarter, and more enjoyable.
Examples of how brands use generative AI:
Nissan Saudi Arabia
Nissan Saudi Arabia used Infobip’s generative-AI capabilities to create a unique, voice-driven experience on WhatsApp. Instead of a standard text chatbot, they launched an AI-powered “voice game” where users recorded a sound, the AI turned it into a waveform, and then matched it to the outline of a new Nissan model for a chance to win a prize. This blended voice recognition, real-time content generation, and gamification into a fun, interactive campaign. Alongside this, the same AI setup still helps customers explore cars, book test drives, and get support but now with a richer, more engaging Gen AI layer on top.
LAQO
LAQO launched a smart digital assistant “Pavle” via Infobip that uses generative AI to power conversations with customers around the clock. Answering FAQs about policies, processing basic insurance inquiries, and guiding people through claims or administrative steps. The assistant works over WhatsApp so users can get help quickly in a conversational style tuned to LAQO’s brand voice. Around 30% of customer queries are now handled by Pavle automatically, which speeds up response times and lets human agents focus on more complicated cases.
Generative AI and Large Language Model FAQs
Mostly, yes. LLMs are a type of Generative AI because they create new text content based on patterns they’ve learned. But Gen AI is a bigger category that also includes tools that generate images, audio, video, code, and more.
LLMs focus on understanding and producing text. Gen AI is the broader family of technologies that can generate all kinds of content, not just text. So you can think of LLMs as one member of the Gen AI family.
Many modern LLMs can help create things like code, summaries, or structured data that are all still text-based. Some advanced models are “multimodal,” meaning they can work with images or audio too, but that’s because they include extra capabilities, not because traditional LLMs were built for it.
It depends on what you want to create. If you need help with text, writing, answering questions, analyzing documents, an LLM is usually enough. If you want to generate images, videos, music, or other media, then a broader Gen AI tool is the better fit.
Yes, when used with the right safeguards. Businesses can control how models are used, what data they see, and how outputs are checked to keep things secure and reliable.
Not necessarily. Many tools come with simple interfaces, so you can use them just by typing instructions. More advanced uses, like customizing a model, would require technical help.
No. They can speed up creative work and spark new ideas, but humans still guide the vision, make the final decisions, and bring emotional depth and context that AI can’t replace.
