Is ChatGPT a model? No. ChatGPT is the chat, and the model inside it is called GPT.
Model, LLM, chat and agent come up in almost every conversation about AI, and two people using them often don’t mean the same thing. The four terms fit together quite simply, like layers, one around the other. The picture is a teaching simplification. It still helps you place tools and ask the right questions.
The model sits at the core
A model is the result of training: a huge file with billions of numbers, known as parameters. An LLM, a large language model, is one kind of model. It was trained on a vast amount of text and predicts, piece by piece, how a text continues. GPT, Claude, Gemini or the Swiss model Apertus are such language models.
An LLM is one kind of model, not a separate layer.
Apertus shows how large such models are. EPFL, ETH Zurich and the Swiss National Supercomputing Centre CSCS released it in 2025 in two sizes, with 8 and 70 billion parameters.
The “pieces” from the video are called tokens: words, parts of words or punctuation marks. The video deliberately says “piece” rather than “word”, because a model doesn’t work word by word. Why this prediction can sound fluent and still be wrong is the subject of the post AI doesn’t lie. It guesses.
The first shell: the chat
On its own, a model does nothing. It needs a shell through which you can use it. The first is the chat: an input field, a history, maybe a few files. You ask, the model answers.
The distinction has practical consequences. When a colleague says “ChatGPT” got something wrong, it’s worth asking: do they mean the model’s answer or a feature of the interface?
The second shell: the agent
The agent gives the model a goal and tools: searching the web, opening files, running programs. And it lets the model work in a loop: plan, act, check, continue, until the task is done.
In a 2024 guide, Anthropic describes agents as systems in which the language model directs its own process and tool use. The guide distinguishes them from workflows, where predefined code decides when the model uses which tool.
The guide also names the downside. Agents cost more, and errors can compound over several steps. It therefore recommends extensive testing in sandboxed environments and appropriate guardrails.
The lines are blurring
Many chats can now search the web. Then they already work a little like an agent: the model decides for itself whether and what to search for, and works the results into its answer. The picture of layers is therefore not a sharp division but a way to get your bearings. The further out, the more the AI does on its own.
The more the AI does, the closer you look
The line to remember from the video sums up the picture: the model computes, the chat answers, the agent acts.
For course leaders, this leads to two points.
Say exactly what you mean. If you set rules for AI in your course, make clear whether you mean a chat or also tools that search, edit files or run programs on their own.
Look more closely the more the AI does on its own. With a chat answer, you check a text. With an agent, you also check which steps it took, which sources it opened and what it changed. That’s my own view; it fits Anthropic’s warning about compounding errors and the need for testing and guardrails.
The picture is easy to practise in a training session. Draw three nested circles on a flip chart, labelled model, chat and agent. Participants write the AI tools they know on sticky notes and place each one in a circle. The borderline cases are the interesting ones, such as a chat with web search. That is where it pays to ask what the tool does on its own and what you need to check afterwards.
How an AI assistant works with your own documents, and what the language model really sees, is explained in RAG explained: your AI never read the handbook.
If you keep the layers apart, you know where to look more closely.
