Popular LLMs (GPT, Claude, Gemini, Llama)
Part of the AI Engineer Roadmap
In short
There's no single 'best' LLM. Different families shine at different jobs, cost different amounts and come with different rules. Knowing the families helps you pick, compare and switch without panic.
The major model families you'll build with — each with different strengths, pricing, context windows and licensing (proprietary API-only vs. open-weight).
Think of it like this
Picking your playing eleven
A captain doesn't pick the same players for a Test match and a T20. One is steady, one hits big, one is cheap to keep on the squad. Choosing an LLM is team selection: match the model to the format of the job.
Watch it happen
The main families and who makes them
1/5Every family ships several sizes, from small and cheap to large and capable.
The big ideas
What you need to know
Compare on your task
Public leaderboards are a starting point. Build 20 to 50 test prompts from your real use case and score each model.
Price is per token
Input and output tokens are usually priced differently. Small models can be many times cheaper for simple jobs.
Context and modality differ
Check context window size and whether the model accepts images, audio or PDFs before you design around it.
Design to switch
Keep model calls behind one function. Then swapping providers or versions is a config change, not a rewrite.
Save yourself the pain
Common mistakes
Oops
Picking the model your friend said is best and never testing others.
Do this instead
Run the same small test set on two or three models. Rankings flip depending on the task.
Oops
Hard-coding one model name all over the codebase.
Do this instead
Store the model name in config and wrap calls in one helper, so upgrades are easy.
Your mission
Mini model bake-off
Quick check
You need to run an LLM fully on your own servers with no data leaving the building. Which choice fits?
Go deeper
- 1Read the model cards/docs for GPT, Claude, Gemini and Llama to compare capabilities.
- 2Try the same prompt across two or three different models and compare outputs.
- 3Track which model families are commonly used for which tasks in real products.
More topics at this stop
LLM Architecture Overview
Tokenization (BPE, SentencePiece)
Pretraining vs Fine-tuning
Context Window & Attention
Open-Source vs Closed-Source Models
Model Scaling Laws (Overview)
Emergent Abilities (Overview)
Multimodal Models (Overview)
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