Large Language Model (LLM): Definition & Explanation

What is a Large Language Model (LLM)?

A Large Language Model (LLM) is an artificial neural network trained on massive amounts of text data to understand and generate human language. LLMs like GPT-4, Claude, Gemini, and LLaMA can write texts, answer questions, write code, and solve complex tasks.

The term "Large" refers to the number of parameters. Modern LLMs have hundreds of billions of parameters that are optimized during training. The more parameters, the more complex patterns the model can capture.

How Do LLMs Work?

LLMs are based on the Transformer architecture, introduced by Google in 2017. The core is the "attention mechanism," which allows the model to recognize relevant relationships in text, even across large distances.

Training in Three Phases

1. Pre-Training: The model learns from billions of texts (books, websites, Wikipedia) to predict the next words. This develops a deep understanding of language.
2. Fine-Tuning: The model is adapted to specific tasks or formats, such as following instructions or answering questions in a dialogue format.
3. RLHF (Reinforcement Learning from Human Feedback): Humans rate the model's responses, and it learns to prioritize helpful, harmless, and honest answers.

Popular LLMs Overview

GPT-5 (OpenAI)

The GPT series (Generative Pre-trained Transformer) from OpenAI is the most well-known LLM. ChatGPT is based on these models. GPT-5.5 (April 2026) introduced native multimodal inputs and a context window of up to 1 million tokens. On July 9, 2026 OpenAI made the GPT-5.6 family (Sol, Terra, Luna) generally available across ChatGPT, ChatGPT Work, Codex, and the API. On September 22, 2026, GPT-6 Sol and GPT-6 Luna followed as cheaper successors to the Sol and Luna tiers, each with a 1,050,000-token context window, 128,000 output tokens, and a knowledge cutoff of April 20, 2026 (Sol) or May 18, 2026 (Luna). OpenAI's best model overall remains GPT-6 Astra.

Claude (Anthropic)

Claude is known for particularly long context windows and a focus on safety through "Constitutional AI." The current family includes Claude Fable 5.1, Claude Opus 5.5, Claude Opus 5, Claude Sonnet 5.5, Claude Sonnet 5, and Claude Haiku 4.5. Fable 5.1 offers a 1 million token context window, up to 128,000 output tokens, and a June 2026 knowledge cutoff. Claude Opus 5.5 replaced Opus 5 as Anthropic's new leading model on September 22, 2026, also with a 1 million token context window and a June 2026 knowledge cutoff, but at a lower price. Opus 5 remains available in parallel. Claude Sonnet 5.5 joined on September 28, 2026 as a faster, lower-cost complement to Opus 5.5, also with a 1 million token context window and a June 2026 knowledge cutoff. It costs half as much as Opus 5.5, and Sonnet 5 remains available in parallel. Claude Mythos 5.1 uses the same model as Fable 5.1 with fewer restrictive safeguards, but remains limited to vetted Project Glasswing partners.

Gemini (Google)

Google's LLM family ranges from Gemini Nano for mobile devices to Gemini 3.1 Pro for complex reasoning and Gemini 3.8 Flash for fast coding and agentic workflows. Gemini 3.8 Flash Cyber is a separate defensive-cybersecurity variant for vetted defenders in the Fairwind program. Google has not published a separate public API price or context-window value for it. The models are natively multimodal and can process text, images, audio, and video.

Muse and Llama (Meta)

Muse Spark 1.3 is Meta's current proprietary multimodal model for long-running agentic and coding tasks. It supports a 1,048,576-token context window. Meta also publishes open-weight LLMs such as the Llama family and Muse Glimmer.

Qwen (Alibaba)

Qwen 3.8 Max 0902 is Alibaba's September 2, 2026 snapshot for coding, long-horizon autonomous tasks, agent collaboration, and visual analysis. It combines 2.4 trillion total parameters, 95 billion active parameters, and a 1 million-token context window. Qwen 3.8 Flash is a multimodal API model with a native 1-million-token context and global list prices of $0.113 for input and $0.382 for output per 1 million tokens. The open-weight Qwen 3.8 Flash Next combines 125 billion main-model parameters with 51 billion N-gram embedding parameters, activates 6 billion parameters per token, and natively supports 262,144 tokens. YaRN can extend its context window to 1 million tokens. QwenCloud uses the API name qwen3.8-flash for this variant.

Applications of LLMs

• Text Generation: Blog posts, emails, marketing copy
• Programming: Code generation, debugging, code reviews
• Customer Service: Chatbots and automated responses
• Translation: High-quality translations into dozens of languages
• Research: Summarizing documents and extracting facts
• Education: Personalized tutoring and explanations

Limitations and Challenges

Hallucinations

LLMs can generate convincing-sounding but factually incorrect information. They sometimes "invent" facts, quotes, or sources. Therefore, critical review of outputs is important.

Knowledge Cutoff

LLMs have a knowledge cutoff date, meaning they only know information up to a certain point in time. Current events are unknown to them unless they have access to external tools like web search.

Context Window Limitation

Although modern LLMs have large context windows, the amount of text they can process simultaneously is limited. With very long documents, the quality of responses may decrease.

Bias and Fairness

LLMs reflect the biases in their training data. Despite intensive efforts toward fairness, they can reproduce stereotypical or discriminatory patterns.

Using LLMs Effectively

To get the most out of LLMs, good prompts are crucial. Techniques like Chain-of-Thought Prompting can significantly improve the quality of responses.

For developers, APIs from OpenAI, Anthropic, and Google offer the ability to integrate LLMs into their own applications. Costs are typically calculated based on tokens consumed.

Comprehensive LLM Parameter Menu

The following interactive table shows over 60 well-known Large Language Models with their parameter counts. You can search by name, filter by developer, size category or model type, and sort the columns:

Conclusion

Large Language Models have fundamentally changed how we interact with computers. They are powerful tools for text processing, programming, and creative tasks, but not a replacement for human judgment and expertise. Those who understand their strengths and limitations can effectively use them for a variety of tasks.