Why cursor IDE's code completion is not at par with claude code's?
How the implementation of same Sonnet 4 models has changed for them?
I used Cursor Free Trial 2 months back and I will be honest, it made my task very fast. The TAB system took me to next changes efficiently and most of the times the changes were accurate. It spoiled me! I was missing it after the free trial. It worked very well with suggestions.
When I used Cursor in Agent mode, it didn’t work well with the tasks. I gave it a very simple task of replicating a functionality which I already developed. You can assume the task was creating a bloc, event and states and a page. It generated the code but wasn’t following the coding guidelines that were already followed in my example and project.
I didn’t buy the subscription.
Then, I saw Claude Code and there was significant improvement even with the same LLM being used both in Cursor and Claude Code. I gave it a same task as to Cursor and the code changes were accurate without any further adjustments.
This was surprising because Cursor was using same Anthropic model but was not working well.
So, I did what we generally do. I asked AI to give differences. And the knowledge is precious so here I am sharing all of that with you.
System Architecture Differences
Cursor built a general-purpose agent that supports multiple models. They need a whole team for that, plus they trained custom models, plus they need to make a profit on top of paying Anthropic for the underlying models
Using Claude Code is like buying direct from the manufacturer instead of through a reseller.
Claude Code uses the model exactly as Anthropic designed it, with no intermediary processing
Cursor adds a 20% margin on top of Anthropic's rates if you don't bring your own key. Read here
Adds additional layers of processing, context filtering and GUI integration
Has to balance multiple model providers and maintain IDE functionality
Different Approaches
This section will take you little deep into technical details of difference in their approaches. If you find this bit hard, I also have a simpler explanation for this.
Claude Code's Approach: "No Embeddings Needed"
When you point Cline at your codebase, it reads code the way you do – file by file, connection by connection. You're working on a React component. Cline reads it, sees an import, follows it. That file imports another, so Cline follows that too. Each file builds on the last, creating a connected understanding of how your code actually works. No index or embeddings. Just intelligent exploration, building context by following the natural structure of your code.
Think of it like this: Claude Code is like a detective following clues. It doesn't need a map of the crime scene - it just follows the footprints from one room to another, naturally building understanding.
Cursor's Approach: "Pre-Built Maps"
Cursor uses traditional RAG (Retrieval Augmented Generation) with embeddings - to map our English query to relevant code symbols (e.g., class names, method names, code blocks), we can leverage semantic search using vector embeddings.
Think of it like this: Cursor creates a detailed map of your house (codebase) with index cards for every room, but sometimes the map gets outdated when you renovate.
The Knowledge Graph Difference
Claude Code can be enhanced with optional knowledge graphs through MCP plugins:
Deep Graph is an MCP that provides Claude Code with advanced understanding tools for your complete codebase. It adds 6 new tools to Claude Code so it can read code in a much more advanced way, perform semantic searches and node-based searches
Claude Context is a monorepo containing three main packages... Core indexing engine with embedding and vector database integration
Cursor has built-in but limited embeddings that get truncated for performance.
Real-World Impact
Here's why this matters: when you chunk code for embeddings, you're literally tearing apart its logic. Imagine trying to understand a symphony by listening to random 10-second clips. That's what RAG does to your codebase. A function call might be in chunk 47, its definition in chunk 892, and the critical context that explains why it exists? Scattered across a dozen other fragments.
Simple analogy: It's like trying to understand a phone conversation by hearing random 10-second clips vs. listening to the whole conversation from start to finish.
The visual guide above shows exactly how this plays out in practice! The key takeaway is that Claude Code's natural exploration often produces better results than traditional embedding approaches, especially for complex code relationships.
Easier Analogy
How they read your code?
Claude Code
Like a researcher who reads a book by following footnotes and references. Sees an import statement? Follows it. Finds a function call? Goes to its definition. Builds understanding naturally.
Doesn't create a separate summary - just reads and follows the logical flow of your code like a human would.
Cursor
Like someone who creates index cards and summaries of your textbook. Sometimes the summary misses important details or becomes outdated when you change the book.
Faster for simple questions, but may miss connections between different parts of your code.
How They Understand Your Project
Claude Code
Doesn't create a separate "map" of your code. Instead, it explores by following the natural structure - like walking through a house by following hallways and doors.
Cursor
Creates a "map" (embeddings) of your code - like having index cards for every function and file. Uses this map to quickly find relevant pieces.
Warp is a very nice terminal and have been using personally. It is a modern tool.
I am starting to learn the basics of LLMs and everything that made AI work and reach the stage it has become now. I will be sharing more learnings in coming posts in easier language. Subscribe to not miss out.
