Large language models (LLMs) are the technology behind ChatGPT, Claude, Gemini, and almost every AI assistant people use today. They can answer questions, write and edit text, translate languages, summarize documents, and even write code, all from instructions written in plain language.
This guide explains what large language models are, how they work, the key terms you will hear, the most popular LLMs today, what they can and cannot do, and how to try several of them yourself.
A large language model is a type of artificial intelligence trained on enormous amounts of text to understand and generate human language. LLMs are a major breakthrough in natural language processing and the engine behind most of today's generative AI tools.
The "large" refers to two things: the amount of training data, often trillions of words from books, websites, and code, and the number of parameters, the internal settings a model adjusts as it learns. Modern LLMs have billions or even trillions of parameters, which is what lets them pick up grammar, facts, reasoning patterns, and writing styles.
At heart, an LLM does something surprisingly simple: it predicts what text should come next. Give it "The capital of France is" and it predicts "Paris." Repeated over and over, with enough scale and training, that ability turns into answers, essays, summaries, and conversations.
LLMs do not read words the way people do. They break text into tokens, small chunks that are often whole words or parts of words, and turn each token into numbers the model can process. Most LLMs are built on the transformer architecture, which uses a mechanism called attention to work out how every token relates to every other token in the input.
Attention is what gives LLMs their grasp of context. In "The bank was steep," the model links "bank" to "steep" and understands a riverbank, not a financial institution. When it writes a response, it generates one token at a time, each time choosing the most fitting next token based on everything that came before.
Getting a model to that point takes several stages of training, all built on machine learning and enormous computing power:
| Training stage | What happens | Result |
|---|---|---|
| Pre-training | The model reads massive amounts of text and learns to predict the next token | Broad knowledge of language, facts, and reasoning |
| Fine-tuning | It is trained further on smaller, curated examples of good answers | Follows instructions and handles specific tasks |
| Feedback and alignment | People and automated systems rate its answers, and the model learns from that feedback | More helpful, accurate, and safe responses |
A few terms come up again and again when people talk about large language models.
| Term | What it means |
|---|---|
| Token | A small chunk of text, often a word or part of a word, that the model processes |
| Parameters | The internal values a model learns during training; more parameters usually means more capability |
| Context window | How much text the model can consider at once, from a few pages to over a million tokens |
| Prompt | The instructions or question you give the model |
| Reasoning model | An LLM that works through a problem step by step before answering |
| Hallucination | A confident answer that is factually wrong |
Of these, the prompt is the one you control directly, and the way you write it has a big effect on the quality of the answer. Our guide to AI prompt optimization explains how to get better results from any LLM.
A handful of companies lead the LLM race, each with a family of models aimed at different needs.
| Model family | Developer | Known for |
|---|---|---|
| GPT | OpenAI | Versatile all-rounders that power ChatGPT |
| Claude | Anthropic | Careful writing, coding, and long-document work |
| Gemini | Multimodal input and tight integration with Google services | |
| Grok | xAI | Real-time information and an informal style |
| DeepSeek | DeepSeek | Strong reasoning at low cost, with open-weight releases |
| Llama | Meta | Open-weight models developers can run and customize |
Each family releases new versions every few months, usually in several sizes, from fast, inexpensive models for everyday tasks to large flagship models for the hardest work. To learn more about the biggest names, see our guides to ChatGPT, Claude, Gemini, DeepSeek, and Grok.
Because they work with language, LLMs can help with almost any task that involves reading or writing. They draft emails, articles, and reports with an AI writing tool, condense long files with a PDF summarizer, translate between languages, explain difficult concepts, brainstorm ideas, and write or debug code. Through AI chat apps, all of this happens in a natural back-and-forth conversation.
Newer LLMs go further. Many can look at images, handle files that are hundreds of pages long, and use tools such as web search. Research modes like Deep Research use an LLM to plan searches, read dozens of sources, and compile a cited report, turning hours of reading into minutes.
LLMs are powerful, but they have real weaknesses that are worth understanding before you rely on them.
| Limitation | Why it happens | What you can do |
|---|---|---|
| Hallucinations | The model predicts plausible text, not verified facts | Check important claims against reliable sources |
| Outdated knowledge | Training data stops at a cutoff date | Use web search or research modes for current topics |
| Bias | Training data reflects human biases | Review sensitive output critically |
| Context limits | Each model can only consider so much text at once | Break very long tasks into smaller parts |
| Privacy concerns | Prompts may be stored or reviewed by the provider | Avoid sharing sensitive personal or business data |
Used with these limits in mind, LLMs are best treated as fast, knowledgeable assistants whose work still benefits from a human review.
Since each LLM has its own strengths, the most practical approach is to try several. Chat Smith, a free AI chatbot for Android, iOS, and the web, brings models from the leading labs into one app, including GPT-6 Sol, Claude Sonnet 5, Gemini 3.5 Flash, Grok 4.5, and DeepSeek V4 Pro. You can switch between them mid-conversation and compare how each one handles the same prompt.
Chat Smith also includes Web Search for current information and Deep Research for bigger questions, which helps work around an LLM's knowledge cutoff. Free users get limited use, and Chat Smith Pro raises those limits for $9.99 per month or $39.99 per year.
There is no single best LLM for everyone. Fast, lightweight models are ideal for quick questions, rewrites, and summaries, while larger flagship models are worth it for complex reasoning, coding, and long documents. Some people prefer one model's writing style, others value another's accuracy or speed. The most reliable way to decide is to give the same real task to a few models, compare the results, and keep the one that fits your work best.
A large language model (LLM) is an AI system trained on massive amounts of text to predict and generate human-like language, learning patterns of grammar, facts, and reasoning along the way. Training involves feeding it enormous datasets and adjusting billions of internal parameters so it can respond to a huge range of prompts.
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