A large language model (LLM) is a type of artificial intelligence system trained on massive amounts of text so it can understand, generate, and reason with human language. LLMs work by breaking text into small units called tokens, converting those tokens into numbers, and running them through a neural network architecture called a transformer that predicts the most likely next token based on patterns learned from billions or trillions of examples. Well known LLM models include GPT-4, Gemini, Claude, and Llama, each trained on huge datasets of books, articles, code, and web pages. In short, an LLM is a next-word prediction system that has absorbed so much language and world knowledge that its output often reads like genuine understanding.
Key Stats
- GPT-3, released by OpenAI in 2020, used 175 billion parameters, a scale that helped establish the modern definition of a "large" language model (OpenAI, 2020).
- Meta's Llama 3.1, released in 2024, scaled up to 405 billion parameters and was trained on more than 15 trillion tokens, making it one of the largest openly available language models at the time (Meta, 2024).
- 65 percent of organizations said they were regularly using generative AI in at least one business function, according to McKinsey's 2024 State of AI survey (McKinsey, 2024).
What is an LLM in simple terms?
An LLM, or large language model, is a computer program trained on enormous amounts of text so it can predict, generate, and edit language in ways that feel conversational and often accurate. Instead of following hand-written rules, the model learns statistical patterns, which words tend to follow other words, how facts are usually phrased, how questions are usually answered, by studying huge collections of text scraped from books, websites, articles, and code repositories. Once trained, the model can write essays, answer questions, summarize documents, translate languages, and hold multi-turn conversations, all from the same underlying system. This is different from earlier chatbots, which mostly matched a user's words against a fixed script rather than generating original language. The "large" in large language model refers to both the size of the training data and the number of internal parameters, the adjustable values the model tunes during training to get better at predicting text.
How do large language models actually work?
Large language models work by converting text into numerical tokens, passing those tokens through a multi-layer neural network called a transformer, and using a mechanism called self-attention to weigh how every word in a sentence relates to every other word. During training, the model is shown massive amounts of text and repeatedly asked to guess the next token, adjusting billions of internal parameters each time it gets the guess wrong until its predictions align with real language patterns. Many modern LLMs go through an additional stage called fine-tuning, often using human feedback, to make responses more helpful, accurate, and safe. When you type a prompt, the model does not look up an answer in a database, it generates a response one token at a time, each new token chosen based on the probability distribution the model has learned across its training data.
What is the largest language model right now?
There is no single official answer to what the largest language model is, because leading labs such as OpenAI, Google, and Anthropic no longer disclose exact parameter counts for their newest systems. Among openly documented models, Meta's Llama 3.1 405B has held the record for the largest publicly released set of weights, with 405 billion parameters trained on more than 15 trillion tokens, a scale Meta detailed when it released the model in 2024. Size alone does not guarantee the best performance either, newer techniques like better training data curation, longer context windows, and reinforcement learning from human feedback often matter as much as raw parameter count. This is one reason the industry has shifted its framing from "biggest model wins" toward "best model for the task," with smaller, cheaper models increasingly matching larger ones on many everyday tasks.
What are the different types of LLM models?
LLM models generally fall into three broad categories, proprietary models, open-weight models, and small language models built for lighter-weight use. Proprietary LLM models, such as GPT-4 and later OpenAI releases, Google's Gemini family, and Anthropic's Claude family, are accessed through paid APIs and are not available for anyone to download or inspect directly. Open-weight LLM models, including Meta's Llama series, Mistral, and Google's Gemma, publish their trained parameters so developers can download, fine-tune, and run them on their own infrastructure. Small language models (SLMs), a newer category, are compact versions built to run efficiently on a phone, laptop, or embedded device while still handling common tasks like summarization and drafting text.
| Aspect | Large Language Models (LLMs) | Small Language Models (SLMs) | Rule-Based NLP | Classic Machine Learning |
|---|---|---|---|---|
| How it works | Neural network with billions of parameters predicting text | Same transformer approach, scaled down to millions or a few billion parameters | Hand-written grammar rules and keyword matching | Statistical models trained on labeled, task-specific data |
| Training data | Trillions of tokens from the web, books, and code | Smaller, often curated or distilled datasets | No training data, rules written manually | Labeled datasets built for one specific task |
| Flexibility | Handles many tasks without retraining | Good for a narrower set of tasks | Limited to scenarios explicitly programmed | Limited to the single task it was trained for |
| Typical cost | High to train, moderate to run via API | Low, can run on a laptop or phone | Very low compute cost | Low to moderate compute cost |
| Example | GPT-4, Llama 3.1, Gemini, Claude | Phi-3, Gemma 2B, Llama 3.2 1B | Early spam filters, scripted chatbots | Credit scoring models, email spam classifiers |
What can businesses do with large language models?
Businesses use large language models to automate writing, summarize long documents, power customer support chatbots, draft and review code, and analyze unstructured text such as reviews, tickets, and contracts far faster than manual work allows. Marketing teams use LLMs to generate first drafts of blog posts and ad copy, support teams use them to answer common questions instantly, and engineering teams use them to speed up coding and debugging. McKinsey's 2024 State of AI survey found that 65 percent of organizations were already using generative AI regularly in at least one business function, which is exactly this kind of writing, support, and analysis work. Because connecting an LLM to a company's own data, tools, and workflows involves real engineering work around security, accuracy, and system design, many companies choose to work with specialized partners for LLM integration services rather than building the connective infrastructure from scratch. Done well, this integration turns a general-purpose model into a system that understands a specific business, its documents, and its customers.
What are the biggest limitations of LLMs?
The biggest limitations of LLMs are hallucination, outdated knowledge, high compute costs, and a lack of persistent memory or true reasoning between separate conversations. Hallucination means the model can generate confident, fluent, and completely incorrect information, since it is predicting plausible text rather than checking facts against a verified database. Every LLM also has a training cutoff date, so it will not know about events, products, or changes that happened after that point unless it is connected to live data sources. Retrieval-augmented generation, where a model is connected to a trusted external database before answering, is one common way teams reduce hallucination risk in production. Most LLMs also do not retain memory of past conversations by default and can struggle with multi-step logical reasoning, which is why many production systems pair an LLM with external tools, databases, and verification steps rather than trusting raw output alone.
How much does it cost to build or use an LLM?
Building a frontier LLM from scratch can cost tens of millions of dollars in compute, while using an existing LLM through an API can cost a fraction of a cent per request. Stanford's HAI Institute estimated in its 2024 AI Index Report that training compute for GPT-4 cost roughly 78 million dollars and training compute for Google's Gemini Ultra cost roughly 191 million dollars (Stanford HAI, 2024). Most businesses never need to train a model from scratch, instead they pay per token to call an existing model's API or, for open-weight models, pay only for the servers needed to run inference. This difference in cost structure is why so many companies now build products on top of existing LLM models rather than attempting to train their own.
How is LLM technology likely to evolve next?
LLM technology is moving toward smaller, more efficient models, longer context windows, and tighter integration with external tools and live data rather than simply chasing larger parameter counts. Multimodal LLMs that handle text, images, audio, and video within a single model are becoming standard rather than a novelty. Agentic systems, where an LLM plans and executes multi-step tasks using external tools, calculators, and databases, are increasingly replacing single-turn chat as the dominant way businesses deploy these models. As competition increases, price per token has continued to fall while quality has continued to rise. For businesses, this generally means better results at a lower price every year, rather than needing to wait for one dramatic breakthrough.
Frequently asked questions
What does LLM stand for?
LLM stands for large language model, a type of artificial intelligence system trained to understand and generate human language.
Is ChatGPT a large language model?
ChatGPT itself is not an LLM, it is an application built on top of LLMs such as GPT-4 and later OpenAI models, which handle the actual language understanding and text generation.
How many parameters does GPT-4 have?
OpenAI has not officially disclosed GPT-4's exact parameter count, though it is widely understood to be substantially larger than GPT-3's confirmed 175 billion parameters.
Can a large language model think or reason like a human?
No, an LLM does not think or reason the way a human does, it generates text by predicting statistically likely word sequences based on patterns found in its training data.
What is the difference between an LLM and a chatbot?
An LLM is the underlying AI model that generates language, while a chatbot is the interface or application, such as a messaging window, that uses an LLM to hold a conversation with a user.
Are open source LLM models as good as proprietary ones?
Open-weight LLM models such as Llama 3.1 have closed much of the performance gap with proprietary models, though the largest proprietary models often still lead on the most demanding reasoning benchmarks.
Updated July 2026. This article will be revisited as new LLM models, benchmarks, and pricing become available.
Need an LLM connected to your own data and workflows? See Codioo's LLM integration services.