Best AI Code Editor: Cursor vs Windsurf vs Replit

Making an app without coding skills is highly trending right now. But can these tools successfully build and deploy an app?

To answer this question,  we spent three days testing the following agentic IDEs/AI coding tools: Claude Code, Cline, Cursor, Windsurf and Replit Agent. We prepared 2 tasks for the agents: API development and app building.

We also compared Cursor, Windsurf, Replit Agent across several key dimensions to compare their real-world AI capabilities. These dimensions included:

• AI agent behavior 
• Code generation and quality
• Context awareness
• Multi-file support

Benchmark results

See our benchmark methodology.

Prompt-to-API benchmark

None of these tools could build a correctly functioning API from a Swagger API documentation with a single prompt.

We attempted to create the API 2 times with the same Swagger file and prompt:

• First attempt:
  • Claude Code cannot deploy the API to Heroku.
  • Cline failed to create an API with the correct endpoints.
  • Cursor failed to create an API.
  • Windsurf created the API but its API failed our unit tests for some of the endpoints. 10/15 endpoints are working correctly.
• Second attempt:
  • All failed to create a working API.

Replit agent does not support creating an API based on our specifications. Since it did not support Laravel Lumen and Heroku, it suggested an alternative way with the same API functionality. We did not accept the alternative solution to keep the benchmark as fair as possible.

During the API creation process, Cursor and Windsurf initially attempted to use PostgreSQL Hobby Dev for the Heroku deployment. However, this revealed a limitation in their knowledge of current Heroku add-ons, as Hobby Dev is no longer supported. Eventually, both tools managed to correctly identify and configure PostgreSQL Essential 0 tier, which is currently Heroku’s most economical PostgreSQL offering.

This demonstrates how these AI tools can adapt their recommendations, though there might be a delay in their knowledge of platform-specific changes in service offerings.

Cline created a correctly working API, but its endpoints were different than the prompted documentation, so we rated it as 0.

All tools offer agentic features, which means they can autonomously perform multiple development tasks. These include writing code, creating file structures, modifying existing code, and generating terminal commands. They can also execute terminal commands and display their outputs directly in their chat interface, making the development process faster.

We did not try to create a UI for this task. If you are interested in screenshot-to-code benchmarks and prompt-to-website benchmarks, you can see our articles.

App building benchmark

We tried to build a basic to-do app since it is one of the first apps every developer builds. Claude Code is the leader of this task. See the results of this task below:

Claude Code

Claude Code is the most successful one, the only functionality missing was the drag-and-drop feature of the tasks.

Cline

Cline was able to code the app, but the buttons were not working, so we didn’t test it to see the functionalities.

Cursor

Coding the app with Cursor was unsuccessful, we tried for more than an hour and it could not provide an app. Since Cursor couldn’t manage to solve the problem after our 5 error-solving attempts, it failed in this task.

Windsurf

Windsurf Editor coded the app in almost 20 minutes. It failed to create an appealing UI. Drag and drop functionality, task editing, and import and export buttons are not correctly working.

Replit Agent

Replit Agent was the fastest, it coded the app in almost 5 minutes. At first, the app looked fine, but when we tested it we saw there were some missing features and functionalities.

For example, when we checked a task as done, all other open tasks were also marked as complete and their contents were overwritten. We decided to share up to 5 such errors with the agent. However, the agent couldn’t debug these errors.

AI agent behavior

This capability demonstrates how effectively an AI assistant can operate within the IDE, executing commands, managing files, and performing project-wide tasks with minimal user input.

Cursor

In Composer, pressing ⌘. activates the Cursor Agent, which functions as an integrated AI assistant within the IDE. It automatically retrieves relevant context from the project, executes terminal commands, and manages files.

Cursor offers AI assistance with a focus on user control. 

In Composer mode, users choose which files to include in the context. AI suggestions (e.g., code suggestions) are previewed as diffs that can be reviewed and applied individually. So that, you can compare the content of files and reports for differences.

Changes remain in the Composer diff view until the user clicks apply or accepts them. Only after approval are the edits written to the project files, meaning the results cannot be previewed or run in real time beforehand.

At the end of the agent response, you can click the review changes button to see the full diff of the changes.

You can turn Compoer on from settings. You also have all the other forms of code generation like AI auto-complete, AI chat, and in-place code editing. All of these features use the diff mode to apply code changes to your files. You can see a full list of the released features here: https://www.cursor.com/features

Windsurf

Windsurf’s agent system, Cascade AI-generated changes are automatically saved to the local project files before user approval, allowing the results to appear instantly in the development server for real-time preview.  If adjustments are needed, changes can be iterated or reverted to a previous state with a single action.

With Cursor’s Composer, you provide instructions and it proposes edits across multiple files; you review and accept those edits before they are applied.

With Windsurf’s Cascade, the system is more agentic and can propagate multi-file changes more autonomously, but still typically presents the changes for your review and acceptance (though the workflow feels more automated).

Replit Agent

Replit’s agent demonstrates a high level of autonomy focused on end-to-end project creation. When given a natural language prompt such as “Build a blog with user authentication,” it scaffolds a complete application from scratch. The agent generates a full file structure (for example, routes/, models/, server.js), installs dependencies (package.json), and connects the components so the project can run immediately within Replit’s container environment.

Windsurf integrates directly into the local editor, where its Cascade mode applies AI-generated code changes in real time for review and approval.

Replit’s Agent, on the other hand, operates inside a fully isolated cloud workspace on the Replit platform. Within this self-contained environment, it can autonomously generate, execute, and deploy complete applications from a single natural-language prompt. The workspace includes all necessary dependencies and compute resources, eliminating the need for local setup or manual approval and enabling an end-to-end, automated approach to application development.

Replit generated a complete Flask app

Code generation and quality

We conducted an AI coding benchmark testing suite to check compliance, code quality, code amount, performance, and security automatically for four programs generated by each assistant. In the benchmark Replit Agent ranked highest. Also code generation speed and depth are key aspects to consider while evaluation AI code editors, so we will examine these for each tool in detail below:

Cursor

In one industry test conducted by Qodo, Cursor demonstrated its capacity for instantaneous code suggestions.

Cursor handled structural patterns correctly but struggled with domain-specific validation logic spanning multiple modules. Below you can see an example where it generated a valid structure for the CustomSerializer class but failed to capture the underlying business rules:

In another test, to further evaluate Cursor’s code generation capabilities in a real-world front-end scenario, a Next.js homepage was created using a React UI library for styling and layout. A short prompt was entered directly in Cursor’s editor, asking it to generate a homepage for a business website with a clean and intuitive interface.

The generated code produced a complete homepage structure with a header, main content card, and typographic elements formatted for readability. The hierarchy was clear.

Windsurf

In Qodo’s test, Windsurf took slightly longer to generate code than Cursor, but delivered results that were better aligned with the broader project context.³ It identified existing helper functions, reused them effectively, and suggested refactors to reduce duplicate logic across services.

Windsurf automatically inserted the correct validation calls based on imports from other modules and flagged potential side effects. It also recommended error handling consistent with team conventions, showing a stronger understanding of project-wide structure.

Thus, Cursor generates faster results but focused on isolated logic. Also several user on Reddit claims Cursor does not understand whole codebase at all often missing dependencies⁴ We can say that, in large, multi-module projects, Windsurf maintained high code integrity.

Replit Agent

Compared to Cursor or Windsurf, which rely on incremental user confirmation or context-bound edits, Replit’s agent can design, code, and test runnable applications from a single prompt without requiring an existing codebase. 

The app testing mode is one of Replit’s core strengths. In this mode the agent opens an interactive environment, clicks through UI elements, and performs real functional testing. 

Another core strength is long-running autonomy. Through max autonomy mode, Replit can execute extended tasks such as full production builds or multi-stage refactors without additional prompts. Here is the official documentation for agent testing and max autonomy mode.

Replit Agent also supports checkpoints with stage-level snapshots of each step (planning, coding, testing, fixing) that can be reviewed in the agent timeline.

Replit checkpoints

Free users can only create 3 apps and have 10 checkpoints. Each conversation with the Agent, including modifying a function or regenerating an app, counts as 1 checkpoint as long as the conversation is executed once.

Context awareness

Cursor

Cursor introduces a unique @ symbol system that lets you directly reference different types of context during AI interactions, whether through ⌘ K, Chat, or Composer. You can also paste external links prefixed with @  to have Cursor automatically fetch and integrate the referenced resource.

Here are some examples of how the @ system works:

• @Files:  Reference specific files in your project.
  
• @Folders:  Pull in entire directories for multi-file reasoning.
  
• @Code: Include specific code sections or snippets.

However,  real-world experiences show that its performance can be inconsistent:

For example, Cursor’s AI chat abruptly loses all context in the middle of a chat.

Context was purged on step 3 of a 4 step form. Cursor has no idea it is already complete with step 1.⁵

The new conversation summarizer constantly causes the AI to drift, lose its working documents:

Source: Cursor Forum⁶

Windsurf

Like Cursor, you can use @ to reference files, folders, or functions directly within Windsurf Chat. 

How it differs from Cursor:

In addition to supporting @-mentions, Windsurf offers deeper automatic context awareness than Cursor. For example:

Both Windsurf and Cursor support broad context-indexing of your codebase. Windsurf uses an automated indexing engine that scans your entire workspace and uses that map for autocomplete and chat. By contrast, Cursor uses explicit prompts (e.g., @-mentions) to bring in relevant context, making the context-gathering more manual.

The project-wide context and Cascade Agent extend this further by continuously tracking edits, terminal commands, and interactions in real time. It tracks your past actions, stays in sync with your flow, and predicts your next move without requiring explicit prompting. It continuously monitors:

• File tracking
• Terminal activity
• Clipboard and chat history

For more, you can see features of Cascade agent here.

Note that  Composer in Cursor also supports multi-file context and structured code generation. However, Composer lacks the real-time, project-wide awareness and autonomous tracking capabilities 

Windsurf’s Cascade Agent maintains real-time awareness, tracking your recent actions so you can simply say “Continue” without re-prompting context.

Replit Agent

Replit’s approach to context awareness differs fundamentally from Cursor and Windsurf. While those tools rely on file-based context, Replit Agent relies on runtime context awareness.

This means that it runs the app in a live environment, providing full visibility into UI behavior, API performance, and logic validation before deployment.

Also, its dynamic Intelligence feature adds enhanced context awareness, iterative reasoning, and goal-driven autonomy via:

• Extended thinking: deeper, slower reasoning (shows partial steps).
• High-power model: higher accuracy when needed.
• Web search: fills knowledge gaps with targeted lookups.

Multi-file support

Cursor

Cursor supports multi-file edits via Composer.

You need to manually select which files to include in the context before it can reason or modify them. It also supports a multi-file editing interface / shortcut (CMD+I / CTRL+I) to invoke cross-file edits.

It can apply coordinated changes across these files, but its logic still depends on explicit triggers or shortcuts.

Experimenting with cross-file editing in @cursor_ai, this is the first multi-file editing UX⁷

Windsurf

Windsurf Cascade monitors user activity (edits, terminal commands, clipboard, and conversation history) to build context in real time. It retrieves context from the entire codebase, performs multi-file edits, and allows developers to “accept multiple changes at once.”

Demonstrates Cascade’s ability to allow you to Create multi-file multi-edit changes in a manner that is self-consistent, accept multiple changes at once

Windsurf’s Cascade Agent can apply coordinated edits across multiple files in a single operation. For instance, it can refactor logic in a backend service, update corresponding imports in related modules, and adjust connected UI components simultaneously. 

Note that, Cursor Agent can make changes across multiple files and run commands, but its documentation specifies user-approved planning and user-initiated workflows. Many actions require confirmation before execution.

Source: Winfsurf⁸

However, performance might decrease with very large files exceeding 1,000 lines:

Source: Reddit⁹

Replit Agent

Windsurf (via Cascade) and Cursor (via Composer) build and maintain static project indexes, these map functions, imports, and relationships across files so the AI can reason about the codebase without running it.

Replit Agent 3, on the other hand, doesn’t primarily rely on static indexing. Its approach remains execution-centered, focusing on live analysis and dynamic dependency tracking.

For example, when given a prompt like “Build a task manager,” the agent generates an entire multi-file structure: backend, frontend, configs, and dependencies, and keeps them synchronized during testing.

Thus, it’s not a good fit for modifying or refactoring existing large repositories.

Benchmark methodology

Prompt-to-API benchmark methodology

This benchmark uses Cursor’s Composer mode and Windsurf Editor’s Cascade mode, with Claude Sonnet 3.5 as the LLM.

Cline and Claude Code used with Claude Sonnet 3.7, and the task will be updated with Claude Sonnet 3.7 used in Cursor and Windsurf.

Prompt: I have a Swagger API Documentation export file (library.json) that defines my API specification. Please help me create a Laravel Lumen Micro REST API based on this specification that will be deployed to Heroku.

We only prompt the tools once with our Swagger file and allow them to use their agentic features. They were expected to build and deploy the app.

Our Swagger file was prepared carefully to cover the whole API without any mistakes.

Please note that we did not make any further prompting to create a working API, since it will harm the objectivity of this task.

App building benchmark methodology

Our prompt:

Todo App Development Requirements

Create a modern, responsive Todo application using React with the following specifications:

Core Features

1. Task Management

• Add new tasks with title and optional description
• Mark tasks as complete/incomplete
• Edit existing tasks
• Delete tasks
• Bulk actions (select multiple tasks for deletion or status change)
• Rich text support for task descriptions

1. Task Organization

• Categories/Labels for tasks
• Priority levels (High, Medium, Low)
• Due dates with reminder functionality
• Sort tasks by different criteria (due date, priority, status)
• Filter tasks by status, category, and priority
• Search functionality for tasks

1. User Experience

• Drag and drop reordering of tasks
• Keyboard shortcuts for common actions
• Responsive design (mobile-first approach)
• Dark/Light theme support
• Loading states and error handling
• Animations for task actions

1. Data Management

• Persist data in localStorage
• Export/Import task data (JSON format)
• Undo/Redo functionality for actions
• Data validation and sanitization

Technical Requirements

Frontend

• Use React 18+ with TypeScript
• State management with React Context or Redux Toolkit
• Styling with Tailwind CSS
• Form handling with React Hook Form
• Date handling with date-fns
• Schema validation with Zod
• Testing with Jest and React Testing Library

Component Structure

1. App Container

• Theme provider
• Global state provider
• Router setup

1. Task Components

• TaskList (main container)
• TaskItem (individual task)
• TaskForm (add/edit task)
• TaskFilters (filtering options)
• TaskSearch (search functionality)

1. UI Components

• Button (reusable)
• Input (reusable)
• Modal (for edit/delete confirmations)
• Dropdown (for filters/sorting)
• Checkbox (for task completion)
• Toast notifications

Data Structure

Features Implementation Order

1. Basic task CRUD operations
2. Task status management
3. Categories and priorities
4. Filtering and sorting
5. Search functionality
6. Drag and drop reordering
7. Data persistence
8. Theme support
9. Keyboard shortcuts
10. Export/Import functionality

Non-functional Requirements

1. Performance

• Optimize rendering with React.memo where needed
• Implement virtualization for large lists
• Lazy loading for non-critical components

1. Accessibility

• ARIA labels and roles
• Keyboard navigation
• High contrast mode support
• Screen reader friendly

1. Code Quality

• ESLint and Prettier configuration
• Git hooks with Husky
• Consistent code formatting
• Comprehensive documentation
• Unit and integration tests

1. Error Handling

• Graceful error boundaries
• User-friendly error messages
• Logging mechanism
• Retry mechanisms for operations

Additional Considerations

• Implement proper loading states for async operations
• Add confirmation dialogs for destructive actions
• Include proper input validation and error messages
• Implement proper debouncing for search
• Add tooltips for action buttons
• Include empty states for lists
• Add proper focus management
• Implement proper color contrast ratios

Please implement the features in the order specified, ensuring each component is properly tested before moving to the next feature. Follow React best practices and ensure the code is well-documented.”

Todo App Benchmark Scoring (100 points)

Basic Features (35 points)

• Add task: 5
• Edit task: 5
• Delete task: 5
• Mark complete/incomplete: 5
• Multi-select/Bulk actions: 5
• Search tasks: 5
• Sort tasks: 5

Advanced Features (35 points)

• Categories/Labels: 7
• Priority levels: 7
• Due dates: 7
• Filter by status/category/priority: 7
• Drag and drop reordering: 7

UI Features (30 points)

• Responsive design (mobile/desktop): 10
• Dark/Light theme toggle: 5
• Animations for actions: 5
• Keyboard shortcuts: 5
• Export/Import data: 5

Our only intervention in the coding process was sharing the errors (up to 5 errors) with the agents or saying “Continue please” when the agents asked us whether to continue.

Pricing

Monthly pro plan costs of the tools as of January 2025:

• Windsurf Editor by Codeium: $15
• Claude Code: $3.6 for two tasks, it is an API-based pricing.
• Cursor: $20
• Replit: $25
• Cline: $4.9 for two tasks, it is an API-based pricing.

To learn the features of these tools, you can read our article about AI coding assistants.

What are the best practices?

To preserve the objectivity of this benchmark, we did not engage in further prompting and debugging. In reality, getting better results is possible with prompting to solve problems.

Preparing detailed documentation helps tools to create better apps.

Knowledge of coding, databases, and deployment options helps get better results.

These tools can be used to help developers get the best results.

Next steps

We will add more tasks to explore their abilities and limits further.

FAQ

More on AI coding:

• Vibe Coding
• Model Context Protocol (MCP)
• AI Coding Benchmark

Reference Links

1.

Windsurf vs Cursor: AI IDEs Tested and Compared [2025] - Qodo

qodo

2.

Windsurf vs Cursor: AI IDEs Tested and Compared [2025] - Qodo

qodo

3.

Windsurf vs Cursor: AI IDEs Tested and Compared [2025] - Qodo

qodo

4.

https://www.reddit.com/r/cursor/comments/1jn9hkv/frustrating_experience_with_cursor_i_dont_want_to/

5.

Context dropout mid response - #4 by khmn54 - Bug Reports - Cursor - Community Forum

6.

Context compressor killing all memory of ai - Bug Reports - Cursor - Community Forum

7.

Multi-file edits - 0.37 update - #3 by deanrie - Feedback - Cursor - Community Forum

8.

https://www.youtube.com/watch?v=aww-BUzL9Lc&t=48s

9.

https://www.reddit.com/r/Codeium/comments/1kc26rp/windsurf_creates_big_files_1000_lines_then/

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Researched by

Şevval Alper

AI Researcher

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Şevval is an AIMultiple industry analyst specializing in AI coding tools, AI agents and quantum technologies.

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