What is the difference between an LLM and an AI chatbot?

An LLM is the underlying model that generates text, while a chatbot is a product built around one, adding a chat interface, memory, and tools. The same model, like GPT-4 or Claude, can power many different chatbots or be called directly through an API.

Should I use a proprietary or an open-source LLM?

Proprietary models like GPT and Claude usually lead on reasoning and are simplest to call via API, but you rent access and send your data to the vendor. Open-weight models like Llama and Qwen can be self-hosted for privacy, lower per-token cost at scale, and offline use, at the price of running your own infrastructure.

Do large language models make things up?

Yes — LLMs can produce fluent but false statements, called hallucinations, because they predict plausible text rather than look up facts. Grounding the model with retrieval, supplying source documents, and verifying critical output reduce the risk but do not eliminate it.

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Large Language Models (LLMs) · 1 tools

Best Large Language Models (LLMs) Tools (2026)

Compare large language models — GPT, Claude, Gemini, Llama, and open-weight models — across context window, coding skill, API access, price, and self-hosting.

All Large Language Models (LLMs) tools

DMC - A unified API for open-source AI models from China.

DMC

dmc.cc

A unified API for open-source AI models from China.

What is a large language model (LLM)?

A large language model (LLM) is a neural network trained on massive amounts of text to predict the next token, giving it broad command of language, code, and reasoning. You reach it through a chat app, an API, or a self-hosted copy, and it can follow instructions, answer questions, write and debug code, and process long documents. Examples span proprietary models like GPT, Claude, and Gemini and open-weight families like Llama, Mistral, and Qwen.

What LLMs can do

Instruction following across writing, analysis, and reasoning tasks
Code generation, explanation, and debugging in many languages
Long context windows for whole documents or codebases
API access with streaming, function calling, and JSON output
Multimodal input: images, audio, and sometimes video
Fine-tuning and open weights for private self-hosting

Who uses large language models

Developers building on an API

Call a hosted model to add chat, extraction, or agents inside an app.

Teams comparing frontier models

Weigh GPT, Claude, and Gemini on reasoning, coding, price, and speed.

Self-hosting with open weights

Run Llama, Mistral, or Qwen locally for privacy, cost, or offline control.

Researchers and builders fine-tuning

Adapt a base model to a domain, tone, or task with custom data.

How large language models work

An LLM is trained on huge text corpora to predict the next token, then tuned with human feedback so it follows instructions and stays helpful. At inference it reads your prompt plus any earlier turns and generates a reply one token at a time within a fixed context window. Retrieval, tool calls, and fine-tuning extend it with fresh data, external actions, or domain-specific behavior.