Large Language Models 101: What Are ChatGPT, Claude, Gemini, and DeepSeek?

AI is changing the world, but many people still do not know what they are using

Over the past few years, AI products have become part of everyday life:

• ChatGPT can help write articles, organize information, and answer questions.
• Claude can read long documents and analyze complex material.
• Gemini can work with text, images, and other types of information.
• DeepSeek has introduced more people to the capabilities of Chinese large language models.
• Copilot can help developers write code and work more efficiently.

Millions of people use these tools every day. But ask a simple question:

What exactly is the AI you are using?

Many people would answer, “A chatbot.”

That answer is not entirely wrong, but it is incomplete. Behind products such as ChatGPT, Claude, Gemini, and DeepSeek is not an ordinary software program, but a more complex class of technology: the large language model (LLM).

This article starts with the fundamentals:

• What artificial intelligence is.
• What a large model is.
• How ChatGPT, Claude, Gemini, and DeepSeek relate to one another.
• Why large models have become one of the most important technologies on the internet.

1. What is artificial intelligence?

Before discussing large models, we need a basic definition of artificial intelligence.

Put simply:

Artificial intelligence gives computers abilities that previously required human intelligence.

In the past, computers calculated according to fixed rules, and software executed instructions written in advance by programmers. Today, AI can understand human language, recognize faces, translate languages, generate images, write code, and analyze large amounts of information.

Consider a familiar example. An older voice assistant might respond to “What will the weather be like tomorrow?” by matching keywords and calling a weather service.

With a modern large model, you can say:

Plan a weekend trip for me, taking my budget, the weather, and transportation into account.

The model can interpret the request and produce a complete plan. The key difference is that traditional software behaves more like a calculator, while a large model behaves more like an assistant that can understand and process information.

2. What is a “large model”?

The phrase “large model” is everywhere, but what is actually large about it?

The word “large” mainly refers to three things.

2.1 It learns from enormous amounts of data

People gain knowledge by reading books, taking courses, and accumulating experience. A large model learns in a somewhat similar way, but at a vastly greater scale.

It is trained on large collections of text, code, images, and other data. Through training, it learns patterns in language, relationships between concepts, and ways to solve problems.

A conventional program can answer only the questions its developers anticipated. A large model does not store every answer in advance. Instead, training teaches it:

What kinds of answers are likely to fit particular kinds of questions.

2.2 The model itself is very large

For a beginner, a model can be thought of as the AI system’s “brain.”

A traditional application may contain hundreds or thousands of lines of code. A modern large model contains a huge number of parameters used to compute and represent relationships between words, concepts, and pieces of knowledge.

You do not need to understand the mathematics of parameters yet. A useful first approximation is:

More parameters usually allow an AI system to learn and process more complex relationships.

2.3 It can handle more complex problems

Traditional software is usually built for a fixed task: a calculator processes numbers, translation software converts languages, and a search engine finds web pages.

A large model can perform many different tasks:

• Write articles.
• Summarize reports.
• Analyze data.
• Generate code.
• Provide learning support.
• Assist with creative work.

This versatility is why large models are often seen as an important direction in the development of general-purpose AI capabilities.

3. How are ChatGPT, Claude, Gemini, and DeepSeek related?

People new to AI often wonder whether ChatGPT, Claude, Gemini, and DeepSeek are simply different names for the same thing.

They are not.

They are more like cars made by different manufacturers. Toyota, BMW, Tesla, and BYD all make cars, but their engines, design philosophies, and performance differ. The AI industry works in a similar way: different companies develop their own models and products.

| Company | Model or product | | --- | --- | | OpenAI | GPT models and ChatGPT | | Anthropic | Claude models | | Google | Gemini models | | DeepSeek | DeepSeek models | | Microsoft | Copilot products |

4. What is OpenAI’s ChatGPT?

OpenAI is one of the world’s best-known AI companies. ChatGPT gave ordinary users their first large-scale experience of generative AI.

In simple terms, ChatGPT is a conversational product built on large language models. When a user enters “Write an email in English for me,” the model interprets the request and generates an appropriate response.

OpenAI’s GPT model family is an important technical foundation for products such as ChatGPT. However:

ChatGPT is not the same thing as a GPT model.

An easy analogy is that GPT is the engine, while ChatGPT is the car built around that engine.

5. What is Claude?

Claude is a family of large language models developed by Anthropic, as well as an AI assistant built around those models.

Claude is known for long-context document work, enterprise use, safety, and complex analysis. It can read dozens of pages, summarize a contract, or analyze a business report.

Anthropic describes Claude as its large language model family, with capabilities that include text and image input.

6. What is Gemini?

Gemini is Google’s family of AI models.

Google operates major internet infrastructure across search, cloud computing, and data services. As a result, Gemini is designed for more than conversation; it also focuses on text understanding, image understanding, and multimodal capabilities.

“Multimodal” means that an AI system can understand more than text. It can also work with images, audio, and video. Google positions the Gemini family for a wide range of AI tasks and development scenarios.

7. Why has DeepSeek attracted so much attention?

DeepSeek is a Chinese AI company that has drawn significant attention in recent years. Its DeepSeek-V and DeepSeek-R model families have been closely watched across the global AI industry.

One reason is its exploration of both model performance and cost efficiency, along with the publication of model information and technical materials.

For a general reader, the significance is straightforward:

DeepSeek has shown more people that competition in large models is no longer limited to US companies; the global AI industry is developing rapidly.

8. What is Copilot?

Copilot is a family of AI assistant products from Microsoft.

It differs from ChatGPT in emphasis. ChatGPT is closer to a general-purpose AI assistant, while Copilot focuses on bringing AI into software people use every day, including Word, Excel, PowerPoint, and programming tools.

Its goal is to make AI part of the workflow. Creating a business report once required manually collecting information, writing the text, preparing tables, and building slides. AI can now participate in many of those steps.

9. What is the biggest difference between a large model and conventional software?

This is the key to understanding modern AI.

The logic of conventional software is:

A person tells the computer exactly how to do something.

A calculator receives numbers, follows formulas written by a programmer, and returns a result.

A large model works more like this:

A person tells the AI what outcome they want.

When you say, “Write a product description,” the AI does not retrieve one fixed article stored in advance. Through training, it has learned what product descriptions are, which structures tend to work, and what language suits marketing. It then generates content for your request.

10. Why will everyone need to understand large models?

Some people still think AI is only for programmers. It is not.

When the internet first appeared, many people thought websites were only an IT concern. The internet eventually reshaped business, education, entertainment, and the way people work.

AI is following a similar path. It is moving into office software, customer service systems, ecommerce platforms, search tools, enterprise management, and content production.

You do not need to become an AI engineer. But you do need to understand what AI can do and the basic logic behind it. That is how you move from trying a few chat features to using AI effectively.

11. Next: Why does AI need tokens?

We now know that a large model resembles an intelligent assistant with access to a vast amount of learned knowledge. But one important question remains:

How does AI understand a sentence that we type?

People see words, such as “Please write an email for me.” The model does not see those words in the same way. Before text enters the model, it is converted into special units called tokens.

The next article will explain:

• What a token is.
• Why AI does not process written language directly.
• Whether one Chinese character always equals one token.
• How tokens relate to an AI system’s capabilities.