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What Is an LLM (Large Language Model)? A Plain-English Explanation

Published Apr 8, 2026 Updated May 5, 2026 6 min read By Kyle Van Buren

AI-assisted, human-reviewed: Portions of this article were drafted with AI tools for research efficiency. Every claim was reviewed and edited by Kyle Van Buren, Founder of whatiswhat.ai. Learn about our process.

Quick Answer

A large language model (LLM) is an AI system trained on vast amounts of text to understand and generate human language. GPT-4, Claude, and Gemini are all LLMs. They work by predicting the most likely next word in a sequence — billions of times, very fast — which produces coherent, contextually appropriate text.

What Makes an LLM "Large"?

The "large" refers to the number of parameters — the adjustable numerical weights that define what the model has learned. Modern frontier models have hundreds of billions of parameters. More parameters generally mean greater capability, but also greater compute cost to train and run.

Training these models requires processing trillions of words from the internet, books, code repositories, and other text sources. The training process adjusts the model's parameters to get better at predicting text, which incidentally teaches it to reason, write, translate, code, and much more.

How LLMs Actually Work

At inference time (when you ask it a question), an LLM takes your input, converts it into tokens (chunks of text), and generates a probability distribution over all possible next tokens. It samples from that distribution to produce the next token, then repeats — thousands of times — until the response is complete.

This is why LLMs can confidently produce false information (hallucinate): they're fundamentally text completion engines optimized for fluency and coherence, not truth verification.

Major LLMs in 2026

GPT-5 Turbo (OpenAI) — Best general-purpose performance
Claude 4 (Anthropic) — Best for nuanced writing and safety
Gemini Ultra (Google DeepMind) — Best multimodal and Google integration
Llama 3 (Meta) — Best open-source option for self-hosting
Mistral Large (Mistral AI) — Best cost-efficient European option

Important

LLMs don't "know" things the way humans do. They pattern-match based on training data. Always verify important facts from authoritative sources, especially for medical, legal, or financial topics.

Frequently Asked Questions

What is the difference between an LLM and ChatGPT?

ChatGPT is a product built on top of an LLM (GPT-4 or GPT-5). The LLM is the underlying model; ChatGPT is the interface and product layer that makes it accessible.

Can LLMs reason?

Modern LLMs demonstrate impressive reasoning capabilities, especially with chain-of-thought prompting. However, their 'reasoning' is a form of learned pattern matching, not symbolic logic — they can still make errors that humans wouldn't.

What is a context window?

The context window is the amount of text an LLM can 'see' at once. Larger context windows let the model work with longer documents. Modern models range from 32K to 1M+ tokens.