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Large language models (LLMs) are neural networks trained on large collections of text to predict the next token—the next word or piece of a word—in a sequence. That process lets them generate responses, summarize material, draft text, answer questions, and assist with coding. There is no single best LLM for every person or task: compare options using work you actually need done, along with their capabilities, cost, access, privacy terms, and safety controls.
What is a large language model?
Microsoft Learn defines an LLM as “a neural network trained on massive amounts of text data to predict the next token in a sequence.” A token may be a whole word or only part of one. Given a prompt, the model predicts a likely next token, then repeats that process using the prompt and the text it has already generated. The resulting sequence is its response.
This objective helps explain why an LLM can produce natural-sounding prose, but it does not mean the model thinks like a person or verifies every statement. It generates text based on patterns learned from training and the context it receives.
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How do large language models work?
Training and generation
During training, a model learns patterns in text that help it predict what comes next. When you use it, it applies those patterns to the prompt and generates a response token by token. The exact training data, methods, and later adjustments vary by model.
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Why transformers are common
Many widely used LLMs use transformer architectures. NVIDIA describes transformers as neural networks that learn context and meaning by tracking relationships in sequential data. This helps a model interpret how parts of a prompt relate to one another, rather than treating each word as entirely separate.
Text is not the only possible input or output
Some current models can work with more than text, such as images or audio, but support differs between models and products. A model’s capabilities also depend on the interface through which you use it: an app, API, or enterprise platform may expose different features or limits.
What are examples of LLMs?
Examples include OpenAI’s GPT family, Anthropic’s Claude family, Google DeepMind’s Gemini family, and Meta’s Llama family. These names refer to model families, not interchangeable products with identical capabilities. Versions, access routes, features, and terms change, so check the official page for the specific model you are considering.
For instance, Google DeepMind’s Gemini 3.8 Flash model card, dated September 2026, describes evaluations in coding, knowledge work, multimodal capabilities, long-context use, computer use, and scientific reasoning. It lists a no-caching input price of $0.75 per million tokens and output price of $3.75 per million tokens, while noting regular prices of $1.50 input and $7.50 output. These are vendor-listed prices and may change; check the current terms before budgeting.
OpenAI’s GPT-6 Astra page, updated September 29, 2026, reports scores of 57.9% on Terminal-Bench 4.0 and 96.0% on GPQA Diamond. Those are OpenAI-published results for named tasks, not an overall ranking or a guarantee of performance on your work.
What can LLMs do?
- Draft and revise: Create a first draft, adjust tone, or improve clarity.
- Summarize and explain: Condense material you provide or explain a concept at a requested level.
- Brainstorm and answer questions: Generate ideas or respond to questions, subject to the model’s knowledge and the information in context.
- Assist with coding: Help explain code, suggest changes, or debug a problem; generated code still needs review and testing.
- Work with other media: Depending on the model and product, process or generate content involving images, audio, or other modalities.
Google’s Gemini overview gives examples such as writing emails, debugging coding problems, brainstorming, and learning. These are possible uses, not guarantees that a response will be accurate or suitable.
Which LLM is best for your needs?
Choose by the job you need done, not by a single headline score. A model that performs well on a benchmark may still be a poor fit for your language, prompt, workflow, privacy requirements, or budget.
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Give each candidate a representative prompt and compare the results against a trusted reference or your own requirements. Check factual accuracy, whether it follows your constraints, how useful its explanation is, and how long the process takes. If the task is consequential, include a human review rather than treating a fluent answer as proof.
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Check capabilities and practical limits
- Task performance: Try the kind of writing, coding, analysis, or other work you actually do.
- Modality: Confirm that the model and interface accept and produce the media you need.
- Long-context reliability: If you work with lengthy documents, check context limits and test whether the model handles details throughout the material.
- Speed, usage limits, and price: Compare current app or API terms for your expected use. Token-based charges and product limits can change.
- Access: Decide whether a consumer app, API, or enterprise platform suits your workflow.
- Privacy and control: Review data-handling terms, licensing, and available safety controls.
- Hosting and adaptation: Decide whether you need a hosted service or a model you can run or adapt yourself.
Interpret benchmark scores narrowly
Benchmarks measure performance on selected tasks under their stated conditions. They can help identify strengths, but they do not establish a universal winner. Treat vendor-published scores as claims from that vendor, note the date and task, and use your own representative work to make a final choice.
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Fluent answers can be wrong
An LLM can state an unsupported claim confidently, miss context, or provide information that is out of date. For important decisions, verify factual claims against primary sources and keep a person accountable for the outcome.
Knowledge cutoffs are model-specific
A cutoff is not a general property shared by every LLM. For example, Google DeepMind’s Gemini 3.7 Flash model card, accessed October 7, 2026, lists a March 2026 knowledge cutoff and cautions that information in some domains may be limited to January 2025. Check the documentation for the exact model; a cutoff does not by itself establish how well the model handles every topic.
Safeguards reduce some risks but do not remove them
Anthropic’s transparency material describes model-specific risk assessments and safeguards. Such measures are relevant when evaluating a system, but they do not mean risk is absent. Consider what information you provide, how the service handles it, and what human checks your use case requires.
How to learn more about LLMs
For a more technical introduction, O’Reilly lists Hands-On Large Language Models, which covers model architecture, prompting, semantic search, and retrieval-augmented generation. It is optional reading, not a prerequisite for using an LLM; because model catalogs change quickly, a book may not reflect the latest versions or terms.
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