“LLM”, “GPT”, “open model”, “parameters”, “context window”… the language-model world is full of terms that sound technical but matter for choosing the right tool. This is a practical, jargon-free guide to large language models in 2026: the types that exist, what the key concepts mean, and how to pick the one that fits your case.
The quick recap: what an LLM is
A large language model (LLM) is a program trained on massive amounts of text to predict the next word, and from that it learns to write, summarize, translate, code and reason. ChatGPT, Claude and Gemini are LLMs. If you want the basics of how they work, see our guide on what an LLM is. Here we focus on the practical side: which one to use.
The types of models
- Closed (proprietary): ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google). The most polished and, usually, the most capable at the top end. You use them via app or API.
- Open (self-hostable): Llama, Qwen, Mistral, DeepSeek and more. You can run them privately and cheaply at scale; great for control over data.
- By size: the same family often comes in a small/fast version and a large/capable one (like Haiku/Sonnet/Opus). Smaller = cheaper and faster; larger = better at hard tasks.
Key concepts that actually matter
- Parameters: roughly, the model’s “size”. More can mean more capability, but not always — quality of training matters too.
- Context window: how much text it can hold in one conversation. Bigger windows fit whole documents.
- Multimodal: whether it handles images, audio or video, not just text.
- Reasoning: newer models “think” more before answering, improving multi-step problems.
How to choose the right one
- Everyday general use: a closed top model like ChatGPT — complete and easy.
- Coding and long text: Claude is very strong.
- Google ecosystem: Gemini fits naturally in Docs, Gmail and Drive.
- Privacy or cost at scale: an open model you host yourself.
The golden rule: don’t chase benchmarks — pick one for your case, use it thoroughly and compare on your real tasks.
Our reading: the idea that explains it all
- An LLM is a next-word prediction machine, trained on enormous amounts of text. It doesn’t “understand” like you do; it calculates what’s likely to come next.
- Why this mental model is the most useful: once you internalise it, you stop expecting guaranteed truth and start verifying. And you understand why it sometimes hallucinates.
- What still surprises us: that such “simple” concept gives rise to such rich capabilities. But “impressive” isn’t “infallible”.
Our advice: use LLMs for what they’re great at (drafting, summarising, brainstorming) and save your judgement for what can’t be delegated: checking it’s true.
Frequently asked questions
Are more parameters always better?
No. They can help, but training quality, fine-tuning and the task matter as much. A smaller, well-trained model often beats a bigger one for a given job.
What’s the context window for?
It’s how much text the model can consider at once; a large window lets you paste whole documents into a single conversation.
Open or closed model?
Closed top models lead at the hardest tasks; open models win on privacy, control and cost at scale.
Which should I start with?
For general use, ChatGPT; for code and text, Claude; inside Google, Gemini. Try one and compare on your tasks.
Conclusion
- LLMs come in closed (ChatGPT, Claude, Gemini) and open (Llama, Qwen, Mistral) flavors, in different sizes.
- Parameters, context window, multimodal and reasoning are the concepts that matter for choosing.
- Pick by your case — general use, coding, Google ecosystem or privacy/cost.
- Don’t chase benchmarks; compare on your real tasks.
More in the state of LLMs in 2026 and open-source models.
