Comparisons · measured July 2026 · 7 min
Claude Code vs Cursor: who gives the model better file context?
They don't do the same thing. Claude Code shells out to grep in your terminal; Cursor queries a semantic index of the repo you opened. One is literal and reaches anywhere but keeps no map; the other is fast and understands meaning but stops at one folder. Neither is strictly better: they're strong and weak in different places. This is a fair look at both, where each hits a wall, and the layer they can share: one machine-wide index either can call.
TL;DR · Key takeaways
Claude Code and Cursor fetch file context two different ways: Claude Code greps in your terminal (literal, reaches any file, no index); Cursor queries a semantic embedding index (fast, meaning-aware, but scoped to the repo you opened).
Each wins somewhere different, yet both share one blind spot: neither keeps a warm, whole-machine index, so neither answers "does this string exist anywhere on this disk?" quickly.
Timed, the grep path is costly: on a 4.47M-file drive one agent file-hunt ran 6m 57s across 71 tool calls and burned ~58% of the session's tokens; content fell back to ripgrep at 93.8 s.
One shared, always-fresh index either agent calls over MCP closes the gap: 85 µs to find a file (139 µs median), that 93.8 s query down to 16 ms, edits searchable in ~1 ms, ~44 MB at rest, and no LLM in the box.
You keep every tool (Cursor its semantic edge, the terminal its reach) and both gain a fast, literal, whole-machine lookup. Everything local is free forever, no card. Hosting starts at $5.99/mo.
The two mechanisms, drawn fairly
One greps. The other embeds.
"Giving the model file context" is really two different pipelines. Claude Code has no index of its own: when it needs a file it runs the same shell tools a person types into a terminal (grep, ls, read) and reads the disk fresh each time. Cursor builds an index first: it splits the open repo into chunks, turns each chunk into an embedding (a point in space where similar ideas land near each other) and matches your question by meaning. Both are good engineering for the job each was built for. Line them up honestly:
| How each fetches | Claude Code | Cursor |
|---|---|---|
| Fetches by | shell tools: grep, ls, read | chunking the repo into vectors |
| Kind of match | literal: the exact string you asked for | semantic: same meaning, other words |
| Scope | anything the shell can reach | the one workspace you opened |
| Freshness | live: reads the disk right now | re-embeds files as you edit them |
| Where it runs | on your machine | embeddings computed in Cursor's cloud |
| Reusable by | only the agent that ran the command | only Cursor |
Read across and the trade is clear: literal-and-everywhere versus semantic-and-scoped. All of the above is public in each tool's own docs: Cursor's indexing pipeline and its cloud vector store, Claude Code's shell-tool approach to files.
The honest verdict
Better at different things, blind in the same place.
For semantic questions inside the repo you have open, Cursor gives the model better context: that is exactly what an embedding index is for. For a literal string that might live anywhere, Claude Code's shell reach wins, until you time it. Both pay a price, and they leave the same gap.
No index. It re-walks the disk on every query: there is nothing warm to ask, so it guesses, reads, and retries.
On a 4.47M-file drive, one file hunt ran 6m 57s across 71 tool calls and burned ~58% of the session's tokens before it opened the right file.
A single by-name lookup measured 193.5s, 2,276,000× a warm indexed lookup on the same machine.
For content it shells out to ripgrep, the fastest grep there is, which still reads the disk each time: 93.8s on one query here.
The index stops at the folder you opened, a correct design for an in-editor assistant, and a hard boundary.
Sibling repos that ship together, and the non-git ~90% of the disk (configs, docs, datasets, the PDF with the answer) sit outside it.
Embeddings live in Cursor's cloud and only Cursor can query them; Claude Code, Codex and Copilot start from nothing.
It never sees your machine's unpushed, non-git reality, only the workspace it was pointed at.
Neither answers the simplest cross-machine question quickly: does this exact string exist anywhere on this disk? One re-walks the whole drive to find out; the other only ever sees a single repo. The layer nobody keeps warm is the one closest to you: the whole machine.
Methodology: Ryzen 9 9950X3D · 64 GB DDR5 · NVMe · Windows 11 · 4,470,000 files. Claude Code figures are the measured agent runs; the warm baseline is an 85 µs single-file lookup (139 µs median of 20), on an engine validated crash-free: correct ranking, zero errors across ~1,500 queries. Cursor's mechanism and scope are drawn from its public documentation: no time is invented for it. Full per-tool method in How Claude Code finds files and How Cursor indexes your codebase.
Two front doors, one floor
They aren't rivals. They're two doors to the same room.
The grep and the embeddings are different front doors, not competing engines. Underneath, both can plug into one shared, always-warm index of the whole machine, over MCP, the open protocol every major agent now speaks. Cursor keeps its semantic edge inside your repo; Claude Code keeps its shell reach; and both gain a fast, literal, machine-wide lookup they can call in one step. Here is the shape.
Different front doors, one fast floor. Neither agent changes how it works: both just gain a warm, whole-machine index to call.
The layer under both
Index the whole machine. Hand it to every agent.
Interlinked draws the floor in that diagram. It keeps an always-fresh index of every file on the machine (names and contents) across every repo and the non-git 90%, and exposes it to whatever agent you use through one connector. It is not a semantic index and doesn't try to be: it's the fast, literal, exhaustive layer that answers "does this exact string exist anywhere on this machine?" in the time Cursor takes to notice you pressed a key. Complementary to the embeddings, and a warm floor under the shell tools.
And because it sees the whole disk, it carries something neither a per-repo embedding index nor a terminal grep can: a knowledge vault the engine derives by itself (which files change together, which repos ship as a set), plus notes any agent leaves that every other agent reads. On a fresh machine, day one:
For scale on the same drive: that filename lookup takes VS Code's own search about 35 seconds (412,000× slower) and Windows Search a median 67 seconds (~480,000×). Content search runs in the 7 to 9 ms range, with exact phrases around 16 ms. A saved edit is searchable in about a millisecond, under 30 ms worst case. At rest the whole engine sits around 44 MB in Task Manager, and the index on disk stays under 1% of the drive. There is no model anywhere in the box.
NOT A REPLACEMENT FOR CURSOR'S EMBEDDINGS OR YOUR TERMINAL: THE FAST, LITERAL, WHOLE-MACHINE LAYER UNDER BOTH.
Frequently asked
Common questions, answered plainly.
Is this a replacement for Cursor's index or my terminal?
No. It's the layer under both. A fast, literal, whole-machine index either one calls over MCP. Cursor keeps its semantic edge inside your open repo; the terminal keeps its reach. Neither changes how it works: both just gain a whole-machine lookup they didn't have before.
Does it use AI or embeddings?
No model anywhere in the box: it's an index plus a hashmap and a path/id match, not a semantic index, and it doesn't try to be. That's why a single filename lands in 85 µs (139 µs median across 4.47M files), and why nothing ever leaves the machine.
How much faster is it than what agents do today?
The content query that takes ripgrep 93.8 s returns in 16 ms, 58,625× faster on the same drive. A file hunt that ran an agent 6m 57s across 71 tool calls (~58% of its tokens) collapses to one ~16 ms call, roughly 7,200,000× less work. That same filename search takes VS Code about 412,000× longer, and Windows Search a median 67 s (~480,000×).
Does it stay fresh as I edit?
Yes, a saved edit is searchable in about a millisecond, with no manual reindex and no re-embedding step. And it tracks the whole machine, not just the one repo you happen to have open.
What does it cost, and is it heavy?
Everything local is free, forever: the app, whole-machine search, MCP. Hosting starts at $5.99/mo. At rest the whole engine sits around 44 MB, measured on a Ryzen 9 9950X3D indexing 4.47M files.
Do this today
Give both agents the same map.
Install once. One signed installer indexes every file on your machine and auto-configures 19 AI clients: Claude Code, Cursor, Copilot, Codex, Windsurf, Zed and the rest. No JSON editing, no cloud account, no keys.
Keep what works. Nothing here asks you to drop Cursor's index or your terminal. Cursor keeps its semantic edge inside your open repo; every agent gains a fast, literal, whole-machine lookup alongside it.
Scope when you can. Whole-machine when you don't know where something lives; scoped to a folder when you do. Scoped queries return in the microsecond range.
Ask across the machine. Then ask any agent “which repos do I have, and how do they relate?” and see how much lived outside the one repo Cursor could see.
Everything local is free forever, on all your devices, no card. Hosting starts at $5.99/mo. Download for Windows · How Claude Code finds files