Figuring out what LLM examples are isn't just about listing names. It's about understanding which digital brain does what, why it matters for your project, and how to pick the right one without getting lost in the hype. You've got ChatGPT, sure. But then there's Gemini, Claude, Llama, and a dozen others. Each has its own personality, strengths, and weird quirks.

What Are LLMs and Why Do Examples Matter?

Think of a Large Language Model as a hyper-intelligent autocomplete. It's trained on a massive chunk of the internet—books, articles, code, forums—and learns patterns in human language. When you give it a prompt, it predicts what words should come next, generating anything from an email to a poem.

Knowing specific LLM examples matters because they're not all the same. It's the difference between using a Swiss Army knife and a scalpel. Some are brilliant conversationalists (ChatGPT), others are coding geniuses (Claude), and some are designed to be private and self-hosted (Llama). Picking the wrong one means you're either overpaying for power you don't need or struggling with a tool that can't do the job.

The Major LLM Examples by Category

Let's break them down not just by name, but by what they're actually good for. This is where most lists fail—they just alphabetize. We're grouping by purpose.

1. The Conversational All-Rounders (Your Go-To Chatbots)

These are the ones you probably know. They're designed for back-and-forth dialogue, answering questions, and helping with general tasks.

• ChatGPT (OpenAI): The one that started the craze. Its free version (powered by GPT-3.5) is decent, but its paid tier with GPT-4 is where it shines—better reasoning, less nonsense. It's your jack-of-all-trades. Great for brainstorming, drafting, and explanations. Drawback? It can be verbose and sometimes too eager to please, making up facts ("hallucinating") if it's not sure.
• Claude (Anthropic): My personal favorite for long-form work. It has a massive context window (a fancy way of saying it can read a whole novel at once and remember it). Give it a 100-page PDF and ask for a summary? No problem. It's also less likely to produce harmful outputs by design. Feels more careful, sometimes to a fault—it will refuse certain requests outright.
• Gemini (Google): Deeply integrated into Google's ecosystem. If you live in Google Docs, Sheets, and Gmail, it feels seamless. Its strength is its ability to pull in real-time web data (when enabled). The free tier is quite powerful, making it a strong alternative to ChatGPT's free version.

2. The Specialist Models

These LLM examples are fine-tuned for specific jobs, often outperforming the generalists in their niche.

• GitHub Copilot (OpenAI Codex): This isn't a chatbot you talk to. It's an AI autocomplete for code. It suggests entire functions, lines, and comments as you type. For developers, it's a game-changer, learning your project's style. It's an example of an LLM baked perfectly into a tool.
• Perplexity AI: Less a single model, more a search engine built on top of LLMs (like GPT-4). It's the best example of an LLM used for research. It answers your question and cites its sources, linking directly to the articles it used. It cuts through the "I made this up" problem pretty well.
• AI21's Jurassic-2: A strong alternative for businesses needing robust text generation for marketing copy, product descriptions, etc. They offer a lot of control over tone and style.

3. The Open-Source Champions

This is where the future is getting interesting. These models' code and weights are publicly released, so anyone can download, run, and modify them.

• Llama 3 (Meta): The heavyweight of the open-source world. Meta's release of Llama 2 and now Llama 3 changed everything. It's powerful enough to compete with closed models for many tasks. You can run smaller versions on your own laptop, or fine-tune the big ones for your specific needs. This is the go-to for privacy (your data never leaves your machine) and customization.
• Mistral AI's Models: The French underdog that quickly became a leader in the open-source AI model due to its high performance scores. They are often a primary target for vector embeddings for semantic search in Retrieval-Augmented Generation (RAG) pipelines. Mistral's models are known for being smaller and more efficient than Llama but just as capable.
• BERT (Google): An older but crucial example. It's not a generative model like the others. BERT is designed for "understanding" language—great for search, sentiment analysis, and classification. It powers a lot of Google Search under the hood. It shows that not all LLM examples are for chatting; some are the invisible engines of the web.

4. The Multimodal Pioneers

These LLM examples don't just process text. They can see, hear, and sometimes even speak.

• GPT-4V (OpenAI): The "V" stands for vision. You can upload an image—a graph, a photo of your broken bike, a screenshot of code—and ask questions about it. "What's wrong with this engine?" "Turn this sketch into a website." It bridges the digital and physical worlds.
• Gemini (Google): Built from the ground up to be multimodal. It handles text, images, audio, and video smoothly. Ask it to describe a painting or create a story from a series of photos.

How to Choose the Right LLM for Your Needs

This is the part most guides skip. Here's a simple decision framework I use after testing dozens of these models.

1. Define Your Task Precisely: Are you summarizing documents (needs long context), writing code (needs logic), creating marketing copy (needs creativity), or doing research (needs citations)? Nail this first.
2. Check the Context Window: How much text can it process at once? For long documents or complex conversations, Claude or GPT-4 with a 128k context are kings. For short Q&A, it doesn't matter.
3. Consider Cost & Privacy: Is this for a personal fun project or a bank's internal tool? For sensitive data, open-source (Llama, Mistral) on your own servers is the only safe choice. For low-cost experimentation, free tiers are great.
4. Test With *Your* Data: Don't rely on benchmarks. Take 3-5 real examples of your task (e.g., five old customer service emails to categorize) and run the same prompt through ChatGPT, Claude, and Gemini. See which one performs best on *your* work. The differences can be stark.

A common trap is using GPT-4 for everything. It's expensive. I once saw a team spend thousands monthly using GPT-4 to classify simple support tickets, a job a fine-tuned, smaller open-source model could do for pennies. They were wielding a sledgehammer to crack a nut.

Where Are LLM Examples Headed Next?

The race isn't just about making models bigger. It's about making them smarter, cheaper, and more specialized.

Smaller, Faster Models: The trend is toward models that are almost as good as the giants but small enough to run on a phone. This enables real-time, offline AI.

Agentic Workflows: Future LLM examples won't just answer questions. They'll act. They'll be given a goal like "plan a vacation" and will autonomously use tools—browse the web, check flight APIs, book tickets—to accomplish it.

Reasoning & Factuality: The next big leap is improving logical reasoning and reducing hallucinations. Models that can show their work, like a math student, and reliably cite facts.