In this article
Before you start
Why interviewers ask this
The question separates people who use an AI coding tool from people who understand what it is doing. A candidate who can say what the loop is, where the context goes and what the tool is allowed to touch is a candidate who can supervise one, and supervision is the part a team still owns.
The question shows up in conversations about tooling, in system design rounds that include agents, and in rounds where AI help is allowed; the page on the AI-assisted coding interview covers the last case. A weak answer is a feature list from a marketing page. A strong answer names the shared architecture first, then the differences that matter for a decision, then what neither tool removes: review of the result.
What each one is
- Coding agent
- A coding agent is a program that gives a language model tools for reading files, editing them and running commands, and repeats model calls and tool calls until a coding task is finished or the user stops it.
- Rules file
- A rules file is a text file in a project that a coding agent reads as standing instructions, such as coding conventions and commands to run, so the user does not repeat them in every conversation.
Claude Code in plain words
Claude Code is an agentic coding tool from Anthropic that reads your codebase, edits files, runs commands and connects to your development tools (Claude Code overview, October 2026). It runs in the terminal, in Visual Studio Code and JetBrains editors, in a desktop app and in a browser. The overview says each surface connects to the same underlying engine.
Its documentation calls the surrounding layer an agentic harness. A task is a conversation. You describe the result. The tool searches, reads and edits, then runs the tests. You steer by interrupting or typing a correction. You can also script it, for example by piping a log into it in a continuous-integration job, an automated job that builds and tests code on each change.
Cursor in plain words
Cursor is a code editor whose documentation describes it as "a coding agent for building ambitious software" (Cursor docs, October 2026). Its agent works in a side pane of the editor, in an Agents Window, on the web and from a command-line interface (CLI). It can edit code, run terminal commands and use a browser tool (Cursor agent overview, October 2026). Its privacy page names model providers such as OpenAI, Anthropic and Google (Cursor privacy, October 2026). So Cursor is a harness that can call more than one model family. The practical difference from Claude Code is the home of the work: the editor window, with the file beside the agent.
How does a coding agent work?
A coding agent works by repeating three actions: gather context, take an action and verify the result. Claude Code's documentation names exactly these phases and says Claude chains dozens of actions together (How Claude Code works, October 2026). Anthropic's essay on agents separates this from a fixed workflow: in an agent, the model directs its own process and tool use (Anthropic, December 2024). The cost is that every step reads the whole conversation so far: in the illustrative example below, 405,000 tokens in total against a final context of 34,500, so, without caching, input cost and time track the total.
A token is a word fragment, the unit in which a model counts text. The context is the text a model reads at one step, and each agent step adds to it. The context window is a separate limit: the most text a model can read in one call. Claude Code's documentation says it manages context automatically as you approach that limit by compacting the conversation into a summary (How Claude Code works, October 2026). Here the context peaks at 34,500 tokens, so only a far longer task would approach a window.1
How does Cursor find code in a large project, and what leaves your machine?
Cursor documents a local search index called Instant Grep that "builds and queries its index on your machine", and says it does not upload paths or code to build it (Cursor codebase indexing, October 2026). The same page says it does not store embeddings, which are lists of numbers that stand for the meaning of a piece of code. What leaves the machine is the model request: when the agent opens a search match, that file content may be included in the request. Cursor's privacy page says prompts and code context go to model providers, and that Privacy Mode ensures code is never used for training by Cursor or other providers (Cursor privacy, October 2026).
Text description of the diagram
- Claude Code runs in the terminal, editors, a desktop app and the web.
- Cursor runs in the editor, an Agents Window, a command-line interface and the web.
- Claude Code reads a project memory file or the shared instructions file; Cursor reads rules files or the shared file.
- Both snapshot files locally, separate from Git; only Claude Code documents that remote actions cannot be checkpointed.
Knowing it is half the job. Saying it is the other half.
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Start Free Trial →Worked example
The worked example is arithmetic, not a benchmark: it shows why a long agent task is expensive to read, whichever tool runs it. Each step of an agent adds to the context defined above. Assume an illustrative start of 6,000 tokens for the system prompt (the model's standing instructions), rules and tool definitions (descriptions of what each tool does), and an illustrative 1,500 tokens added at every step by tool output and the model's reply.
Step k reads everything so far, so its context is 6,000 + 1,500 × (k − 1) tokens. Cumulative tokens read reach 405,000 by step 20, while the context itself ends at 34,500 tokens. Prompt caching, a vendor feature that bills a repeated prefix at a lower price, would lower the price and processing time of the repeated part, so 405,000 is the token count either way and the uncached upper bound on cost and time. No figure here was measured on either product.1
| Fact | Step 1 | Step 5 | Step 10 | Step 15 | Step 20 |
|---|---|---|---|---|---|
| Context at this step (illustrative tokens) | 6,000 | 12,000 | 19,500 | 27,000 | 34,500 |
| Cumulative tokens read (illustrative) | 6,000 | 45,000 | 127,500 | 247,500 | 405,000 |
Text description of the diagram
- Cumulative tokens read reach 405,000 by step 20.
- The context itself ends at 34,500 tokens.
The total is 20 × 6,000 plus 1,500 times the sum of 0 to 19, which is 190, so 120,000 + 285,000 = 405,000. The growth is quadratic in the number of steps, so doubling the steps to 40 would more than triple the reading in this model. The per-call context, not the cumulative total, is what fills the window.1
Claude Code's documentation says that as you approach the limit it compacts the conversation into a summary that keeps your requests and key code snippets, that detailed instructions from early in the conversation may be lost, and that subagents, helper agents that take a side task and return a summary, work in their own context window, so persistent rules belong in the rules file (How Claude Code works, October 2026). The same effect is described under context rot and context folding for language-model agents.
Trade-offs and edge cases
The two tools overlap more than they differ, so the useful comparison is of documented facts, each tied to a source, plus the cases where the choice changes. Rules files differ in name and place: Claude Code reads a project memory file and can read the shared agent-instructions file in its place, while Cursor reads rules in .cursor/rules and the same shared file (Claude Code overview; Cursor rules). Both snapshot files locally, separate from Git; only Claude Code's documentation states that actions on remote systems such as databases and deployments cannot be checkpointed (How Claude Code works; Cursor agent overview).
| Fact | Claude Code | Cursor |
|---|---|---|
| Where it runs | Terminal, editors, desktop app, web | Editor side pane, Agents Window, web, command-line interface |
| Standing instructions | Project memory file, or the shared agent-instructions file | .cursor/rules files, or the shared file |
| Undo | Snapshots before edits; remote actions not covered | Local checkpoints, separate from Git |
| Permission modes | Auto, manual, accept edits, plan | Not stated on the pages read |
| Training on your code | Not under commercial terms; a user setting on consumer plans | Never, with Privacy Mode on |
| Searching code | Pattern and regular-expression search tools | Local Instant Grep index |
A regular expression is a text pattern, such as one that matches every line starting with "TODO", so a regular-expression search finds code by its shape rather than by one exact word.
Data policy deserves a careful sentence, because it depends on the plan. Anthropic's documentation says that under commercial terms it does not train generative models on code or prompts sent to Claude Code unless the customer opts in, while consumer plans carry a choice, and that standard commercial retention is 30 days (Claude Code data usage, October 2026). Both vendors send your prompts and code context to a model provider; neither runs the model on your laptop by default.
Edge cases: the tools can be used together, since the Claude Code overview lists an extension install for Cursor, and a team that already keeps a shared agent-instructions file can keep one set of instructions for both. This page ran no head-to-head test, so the flow below is a judgement, not a result. If the work starts in a terminal, script or pipeline, start with Claude Code. If it starts in an editor window beside the file, start with Cursor. Otherwise run one real task in both and compare.
Text description of the diagram
- If the work starts in a terminal, script or pipeline, start with Claude Code.
- If the work starts in an editor window beside the file, start with Cursor.
- Otherwise run one real task in both and compare.
How to answer it in an interview
How do Claude Code and Cursor differ, and how would you choose?
Both are coding agents: a model with tools to read files, edit them and run commands, in a loop of gather context, act and verify. Claude Code runs in the terminal and also in editors, a desktop app and the web; Cursor is an editor with the agent inside it. They differ in surface, in the rules file they read and in how they search code. I would choose by where the work starts, then run one real task in both. Either way I review every diff, the line-by-line view of what the agent changed, because the cost that grows with the loop is context, and a long task can drift.
The spoken structure is: shared loop, the real differences, how you choose, then what you still own.
Common mistakes
- "Cursor is a different kind of AI." (wrong) Cursor calls a model chosen by the user, so it is a harness, not a model. (right) "Both are harnesses around a language model; the surfaces differ."
- "The agent sees my whole repository." (wrong) It sees what its tools return, and every file it opens costs context. (right) "It searches, reads what it needs and carries that in its context window."
- "Privacy Mode means nothing leaves my machine." (wrong) Cursor says prompts and code context still go to model providers; Privacy Mode is about training. (right) "Code context leaves for the model request; training is off."
- "A checkpoint undoes everything." (wrong) Claude Code says actions on databases, application programming interfaces (APIs) and deployments cannot be checkpointed. (right) "File edits can be undone; effects on remote systems cannot."
- "The faster tool wins." (wrong) This page measured no speed. (right) "I would run one real task in both and compare the diffs."
Follow-up questions
How do I use Claude Code?
You describe the result you want in plain language, and the tool searches, reads, edits and runs commands; you steer by interrupting or typing a correction (How Claude Code works, October 2026). It runs in a terminal, in Visual Studio Code and JetBrains editors, in a desktop app and in a browser (Claude Code overview, October 2026), and it can be scripted, for example by piping a log into it in a continuous-integration job. Put standing instructions in the project memory file so they survive a long conversation, and review each diff before you accept it.
How does Cursor work under the hood?
Cursor is an editor with an agent that calls a model the user picks and gives it tools to edit code, run terminal commands and use a browser (Cursor agent overview, October 2026). To find code it documents a local index, Instant Grep, that is built and queried on your machine without uploading paths or code, although file content the agent opens can still be included in the model request (Cursor codebase indexing, October 2026). Privacy Mode concerns training, not whether the request is sent (Cursor privacy, October 2026).
What makes a coding agent reliable?
Reliability comes from the verify step and from a person reviewing the result. Claude Code's documentation names verification as one of the three phases, after gathering context and acting (How Claude Code works, October 2026), and a test run gives the agent a result to react to rather than a guess. Clear standing instructions, a small checkable task and undo through Git or checkpoints limit the damage of a wrong step, but none removes the need to read the diff.
What if the task runs 40 steps instead of 20?
In the same model, 40 steps read 1,410,000 tokens in total, about 3.5 times the 405,000 of the 20-step task, and the context ends at 64,500 tokens.1 Doubling the steps more than triples the reading because each step rereads everything before it. The larger context sits closer to the window limit at which compaction starts.
Can you use Claude Code inside Cursor?
Yes. The Claude Code overview lists an install link for Cursor beside the one for Visual Studio Code, and says the extension provides inline diffs, @-mentions (typing @ to point the agent at a file) and plan review in the editor (Claude Code overview, October 2026). Cursor's own agent remains a separate feature of the editor.
Which one should I use in an AI-assisted coding interview?
Use whatever the interviewer allows, and ask first. In such a round, the evaluated skill is how you direct the tool, check its output and explain the result; the AI-assisted coding interview page covers what is typically assessed, and human-in-the-loop AI agents explains where a person must stay in the loop. Related reading: AI agents, agentic retrieval, the Model Context Protocol and observability for language-model applications.
- Both tools are coding agents: a model, tools and a loop of gather, act and verify.
- They differ in surface, rules file, what undo covers and how they search code, not in the loop.
- Every step rereads the context, so total reading, which drives cost and time, far exceeds the final context size; compaction depends on the context nearing the window limit.
- Privacy depends on plan and mode; both send code context to a model provider.
- Choose by where the work starts, test both on one real task, and review every diff.
Sources
- Claude Code overview — Anthropic (date = read date; page undated), 25 Oct 2026
- How Claude Code works — Anthropic (date = read date; page undated), 25 Oct 2026
- Data usage (Claude Code documentation) — Anthropic (date = read date; page undated), 25 Oct 2026
- Cursor documentation — Cursor (date = read date; page undated), 25 Oct 2026
- Agent overview (Cursor documentation) — Cursor (date = read date; page undated), 25 Oct 2026
- Rules (Cursor documentation) — Cursor (date = read date; page undated), 25 Oct 2026
- Codebase indexing (Cursor documentation) — Cursor (date = read date; page undated), 25 Oct 2026
- Privacy (Cursor help) — Cursor (date = read date; page undated), 25 Oct 2026
- Building effective agents — Anthropic, 19 Dec 2024
