How LLMs Generate Code
A large language model is a neural network trained on enormous datasets of text and code. During training, the model learns statistical patterns across billions of examples. During inference (when you use it), the model predicts the most likely next token given everything that came before it.
Code generation works because code is text. The model has seen millions of functions, thousands of design patterns, and countless Stack Overflow answers. When you write a function signature, the model draws on those patterns to predict a plausible function body.
This is also why LLMs make confident mistakes. The model does not understand what code does. It predicts what code that looks right would look like. A function that compiles, passes a linter, and reads correctly can still contain a subtle logic error because statistical likelihood is not the same as correctness.
Models Behind Popular Coding Tools
Every AI coding tool runs on one or more LLMs, and the model choice shapes the tool’s behavior. GitHub Copilot runs on OpenAI models. Claude Code runs on Anthropic’s Claude models. Cursor lets you switch between Claude, GPT-4o, and other models mid-session.
Different models have different strengths. Claude models tend to follow complex instructions more reliably. GPT-4o is faster on short completions. Open-source models like DeepSeek Coder and Code Llama run locally without sending code to an external API.
When a tool changes its underlying model, users notice immediately. Autocomplete speed changes. The style of generated code shifts. Error rates on specific languages go up or down. The model is not a background detail. It is the engine.
What LLMs Cannot Do
LLMs do not execute code, access the internet, or remember previous conversations (unless given explicit memory mechanisms). They generate text. Everything else is built on top of that capability by the tools that wrap them.
When Claude Code runs your tests and reads the output, that is tool infrastructure, not the LLM. The model generates a command. The tool executes it. The model reads the result as text and generates the next response. Understanding this boundary clarifies what the model is responsible for (generation quality) versus what the tool is responsible for (execution, file access, memory).
LLMs also have no concept of “current” knowledge. They are trained on data up to a cutoff date. Code patterns, API surfaces, and library versions that changed after training may produce outdated or incorrect suggestions.
Choosing and Evaluating Models
Most developers do not choose their model directly. They choose a tool, and the tool chooses the model. But understanding the model layer helps explain why the same tool performs differently on different tasks.
Larger models generally produce higher-quality code but respond more slowly and cost more per token. Smaller models are faster and cheaper but make more mistakes on complex logic. Some tools address this by using different models for different tasks. Fast, small models handle autocomplete (where speed matters most), while larger models handle chat-based code generation and multi-file refactoring (where accuracy matters more).
The model market is shifting rapidly. New releases from Anthropic, OpenAI, Google, and open-source communities arrive every few months, and tool vendors update their model selections accordingly. A tool that felt slow last quarter may be noticeably faster this quarter because it switched to a newer, more efficient model. Checking what model your tool currently uses and whether alternatives are available is worth doing at least once per quarter.