If you’ve used ChatGPT, Claude, Gemini or any modern chatbot, you’ve probably heard the term LLM – Large Language Model. Somewhere along the way, many of us quietly started calling every smart AI system an LLM.
But here’s the truth:
LLM ≠ all of AI.
Today’s AI world is more like a toolbox than a single tool. There are different kinds of models, each designed for specific jobs: talking, seeing, reasoning, acting, editing images, working on your device, and more.
In this simple guide, let’s walk through 8 important types of AI models, explained in plain language, so you can finally say, “Ah, that’s what this thing really is.”
1. LLM – Large Language Model 🧠
The “talking” brain of AI
This is the one you already know best.
A Large Language Model is an AI system trained on huge amounts of text so it can understand and generate human-like language. LLMs power chatbots like ChatGPT, Gemini, Claude, and many others, and are great at:
- Answering questions
- Writing emails, blogs, and code
- Translating languages
- Summarising long documents
Technically, they’re deep learning models (usually transformers) trained on massive text data to predict the next word in a sequence. That simple objective makes them surprisingly good at language, reasoning and content generation.
Think of an LLM as:
A super-smart writing and conversation engine.
2. LCM – Large Concept Model 🧠💡
The “big-picture thinker”
While LLMs think word by word, Large Concept Models (LCMs) try to think idea by idea.
Instead of predicting the next token (word piece), LCMs work at the concept or sentence level. They operate in a “concept space” where each concept represents a bigger chunk of meaning, often using multilingual embedding spaces like SONAR.
What does that mean in simple terms?
- LLM: focuses on the exact words and their order
- LCM: focuses on the overall ideas, themes and relationships
This makes LCMs promising for:
- Long reports and research
- Multilingual reasoning (same concept across many languages)
- Summaries that capture the essence, not just the sentences
Meta and others are actively exploring LCMs as a new way to separate reasoning from raw language.
Think of an LCM as:
The “strategy brain” that cares less about words and more about ideas.
3. LAM – Large Action Model ⚙️🖱
The “doing” brain of AI
If LLMs are good at talking, Large Action Models (LAMs) are built for doing.
A LAM is an AI system that doesn’t just reply with text – it can actually take actions in software based on what you ask: click buttons, fill forms, run tools, navigate interfaces and complete multi-step tasks.
Examples and early signs of LAM-style systems:
- Rabbit R1 device, which claims to use a LAM to operate apps on your behalf Toloka
- Agentic systems where a model can open apps, call APIs, and complete workflows
- Features like Claude’s “computer use”, where the model literally moves the mouse and types for you SuperAnnotate
Everyday use cases:
- “Book me a flight for Friday evening under ₹15,000.”
- “Download this report, summarise it, and email it to my team.”
Instead of just telling you how to do it, a LAM-style system aims to do it for you.
Think of a LAM as:
A digital colleague who doesn’t just advise, but also clicks, types, and executes tasks.
4. MoE – Mixture of Experts 👥🧠
A team of specialists, not one giant brain
Mixture of Experts (MoE) is less a “type of AI” you talk to and more an architecture trick used inside some big models.
Instead of one huge neural network, MoE models use many smaller specialist networks (“experts”), plus a gating network that picks which experts to use for each input.
Why this matters:
- You can have many experts, but only a few are active per request
- This gives you the power of a big model, but with lower compute cost
Well-known examples include models like Mixtral 8x7B from Mistral AI, which uses a sparse Mixture-of-Experts design to match or beat much larger dense models while being more efficient.
Think of MoE as:
A panel of specialists where only the right experts are called in for each question.
5. VLM – Vision-Language Model 👁️🗣
The model that can “see and talk”
A Vision-Language Model (VLM) combines image understanding with language understanding.
It can take both images and text as input, and usually outputs text—like captions, explanations or answers.
What VLMs can do:
- Describe what’s in an image (“A boy flying a kite in a park”)
- Answer questions about an image (“How many people are at the table?”)
- Help with document understanding (screenshots, PDFs, charts)
Modern frontier models such as GPT-4o, Gemini, Claude 3/3.5 and many open-source systems include strong vision-language capabilities, combining text and images in one model.
Think of a VLM as:
An AI that can look at something and then explain it to you in words.
6. SLM – Small Language Model 📱
Tiny, fast, and runs close to you
While LLMs live mostly in the cloud, Small Language Models (SLMs) are designed to be lightweight, so they can run on:
- Laptops
- Phones
- Edge devices (IoT, embedded systems)
Companies like Microsoft and Google are actively pushing SLMs such as Phi-3 and Gemma, which deliver strong language and reasoning performance while being small enough for local deployment.
Why SLMs matter:
- Speed: responses can be very fast
- Privacy: your data can stay on your device
- Cost: cheaper to run at scale
Great for:
- On-device writing assistance
- Smart features inside apps (notes, email, calendars)
- Offline or low-connectivity scenarios
Think of an SLM as:
A pocket-sized LLM that lives on your own device.
7. MLM / MLLM – Multimodal (Large) Language Model 🎥🖼🎧
One brain for text, images, audio, and sometimes video
Multimodal AI means one system can work with more than one type of data—for example, text + images, or text + audio + video.
A Multimodal Large Language Model (often called MLLM, here shortened as MLM) is an LLM extended to handle multiple modalities in a single model:
- Understand text + images (e.g., screenshots, photos, charts)
- Listen to audio, transcribe and respond
- Interpret or even generate video in newer systems
IBM and others describe MLLMs as models that can process and reason over text, images and audio, enabling tasks like describing images, answering questions about videos, interpreting charts, and doing OCR.
Real-world use:
- AI tutors that can read your handwritten notes and explain them
- Meeting assistants that handle audio, slides and chat together
- Creative tools that mix text, images and video
Think of an MLLM as:
A “universal input” assistant that doesn’t care if your information is text, a picture, or a clip—it just works with all of it.
8. SAM – Segment Anything Model ✂️🖼
The “cut anything out of an image” model
Segment Anything Model (SAM) is a specialised computer vision model from Meta. Its job:
Given an image and a simple hint (like a point or a box), SAM tries to cut out the exact object you meant.
It was trained on a huge dataset of 11 million images and 1.1 billion masks, and can generalise to many kinds of objects it has never explicitly seen before.
Use cases:
- Medical imaging (highlight a tumour or organ)
- Object tracking in videos
- Graphic design and photo editing
- Robotics and autonomous systems that need to understand objects
Meta has even released SAM 2, tuned for fast and precise segmentation in images and videos.
Think of SAM as:
A laser-sharp “cutting tool” that can outline almost any object in a picture.
So… Why Does This Matter?
If you’re building, buying, or simply using AI products, understanding these model types helps you ask better questions:
- Do I just need text generation → LLM or SLM
- Do I need planning & high-level reasoning → LCM-style systems
- Do I want the AI to actually take actions in apps → LAM / agentic systems
- Am I working heavily with images, audio or video → VLM, MLLM, SAM
- Do I care about cost and speed at scale → MoE architectures
Most serious AI products today are mixing and matching these ideas:
A multimodal LLM… built with MoE layers… wrapped in a LAM-style agent… running partly as a small on-device model.
That’s why it’s no longer accurate to call everything “just an LLM”.
Final Takeaway
Next time someone says, “We’re using an AI model,” you can gently ask:
“Nice! Is it a language model, action model, vision-language model, or something else?”
Because AI isn’t one-size-fits-all.
Understanding these 8 types doesn’t just make you sound smarter—it helps you make better choices about which AI to trust, deploy, and invest in.
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