What are Large Language Models (LLMs)?

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:

A few terms come up again and again when people talk about large language models.

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.

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.

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.