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?
We benchmarked 6 AI code editors across 10 real-world web development challenges. Each task required implementations such as backend, frontend, authentication, state management. We evaluated backend correctness, frontend behavior, and combined performance, and analyzed how each agent operates during execution.
Benchmark results
Cursor achieved the highest backend and combined score and tied with Kiro Code for perfect frontend performance. Kiro Code ranked second overall with strong UI consistency. Antigravity performed strongly on backend tasks and maintained solid frontend behavior.
Roo Code and Replit showed similar backend performance, though Roo Code performed better on frontend evaluation. Windsurf ranked last in both backend and frontend scores.
Tool insights
We benchmarked AI Code Editors on different real-world tasks (see Task 6 on Github as an example) and investigated how they operate.
Cursor
Cursor consistently applies the smallest viable fix. When authentication dependencies conflicted, it removed the failing abstraction layer rather than redesigning the entire subsystem. The architecture remained intact; only the failing component changed.
That pattern reflects a conservative engineering bias. Cursor assumes the system is mostly correct and isolates the failure. It favors incremental stability over architectural rewrite.
Its pricing structure reinforces that positioning. Cursor offers subscription tiers and also provides usage-based expansion through a pay-as-you-go model and Cloud Agents. This aligns with a professional developer audience: a stable baseline subscription and scalable compute when needed. It functions as a productivity multiplier for existing workflows rather than as a full-stack orchestrator.
Cursor’s strength lies in controlled iteration with predictable risk.
Kiro Code
Kiro reacts differently to friction. When dependency incompatibilities appeared, it did not patch around the issue. It replaced the subsystem entirely and normalized hashing across the codebase.
This is a structural bias. Kiro optimizes for internal consistency even if the intervention is larger than strictly necessary. It prefers a clean system over a minimal diff.
Its pricing model reinforces this. Kiro uses a credit-based system tied to execution. This encourages deliberate, spec-driven runs rather than continuous micro-iterations. The economic model matches the technical style: structured, intentional builds instead of rapid-fire terminal adjustments.
Kiro behaves like a specification-driven engineer who prefers correctness by reconstruction rather than containment.
Antigravity
Antigravity’s defining difference is not how it fixes backend bugs. It is how it validates outcomes. Because it can interact with the browser, it evaluates visible behavior rather than stopping at API correctness.
When it adjusts, it does so across surfaces. Backend, frontend, and live preview form a single feedback loop. Its decisions are shaped by what the user sees, not just by what the logs say.
Antigravity is currently offered for free. That matters. The lack of usage-based gating encourages exploratory multi-surface iteration. It is positioned less as a productivity add-on and more as an autonomous build surface.
Antigravity behaves like a full-stack operator, treating user-visible correctness as the final signal.
Roo Code
Roo Code emphasizes structured completion and explicit mapping to acceptance criteria. In the benchmark tasks, it focused on ensuring that every rule in the specification was implemented: correct status transitions, permission boundaries, and proper 404 vs 403 behavior where required.
We did not use Roo Code’s Cloud Agent runtime during this benchmark. However, Roo Code offers an optional cloud execution mode with hourly pricing. This allows tasks to run in a managed environment without turning the editor itself into a subscription-gated tool.
Even without signing up for the Cloud Agent, Roo Code exposes full conversation history and detailed usage breakdowns. This makes cost tracking and auditability straightforward. For benchmarking, that visibility is useful.
Roo Code behaves like a compliance-focused finisher. It optimizes for covering every listed requirement and producing a clean, well-structured output.
Replit
Replit operates in a different architectural context. The IDE, runtime, preview, and hosting layer are unified in the cloud. Its decisions revolve around orchestration rather than local refactoring.
In the benchmark task, it spawned backend and frontend in parallel, managed workflows, restarted services when state drifted, and verified both preview and API behavior. The environment is part of the product.
Replit’s pricing is subscription-based with credits that apply to its AI Agent and platform services. This reflects its positioning as a cloud-native development surface rather than a local IDE augmentation.
Replit behaves like a cloud DevOps coordinator embedded inside the coding loop.
Windsurf
Windsurf escalates into logs more aggressively than most tools. It inspects failure states deeply, isolates schema mismatches, adjusts token structures, and retests endpoints programmatically before concluding.
Its validation is backend-centric and structured. It formalizes acceptance criteria into repeatable checks rather than assuming visual confirmation is sufficient.
Windsurf uses a tiered credit model with add-on purchases. This positions it between lightweight experimentation and professional usage. The economic structure supports structured diagnostic runs rather than unlimited exploratory interaction.
Windsurf behaves like a backend engineer who refuses to conclude without formal proof of correctness.
The differentiating factors between ai coding tools
The benchmark scores are close because all six can code. The meaningful separation lies elsewhere.
- Cursor optimizes for minimal disruption.
- When something breaks, Cursor changes as little as possible. It keeps the structure, swaps out the failing part, and moves forward. It behaves like a careful engineer who does not want to risk breaking other parts of the system.
- Kiro optimizes for structural coherence.
- When something breaks, Kiro is more willing to replace the whole subsystem to keep the design clean and consistent. Instead of patching, it rebuilds that layer properly. It prefers a tidy architecture over a small fix.
- Antigravity optimizes for user-visible correctness.
- Antigravity cares about what the user actually sees. Because it can interact with the UI, it checks whether buttons, flows, and pages behave correctly, not just whether the backend responds with 200 OK.
- Roo Code optimizes for specification alignment.
- Instead of focusing on logs or the UI, Roo Code checks whether every rule in the task description is implemented. For example, if the specification says “customer must receive 404 instead of 403,” Roo Code ensures that the exact rule exists in the code. It behaves like someone checking off each requirement to make sure nothing is missing.
- Replit optimizes for cloud workflow orchestration.
- Replit manages the whole system lifecycle inside its hosted environment. It starts services, restarts them, checks previews, and manages state. It behaves like a coordinator, ensuring the full stack runs smoothly within a single controlled workspace.
- Windsurf optimizes for diagnostic certainty.
- Windsurf digs deeply into logs and error messages. It wants proof that the system is correct. It tests endpoints explicitly and confirms that rules are enforced before it declares success. It behaves like someone who writes and runs tests before shipping.
Pricing models reinforce these behaviors. Subscription-plus-usage models favor professional stability. Credit systems encourage deliberate runs. Free access promotes exploratory iteration. Cloud runtime billing reflects orchestration and infrastructure positioning.
That is the difference between tools that generate code and tools that embody different philosophies of engineering.
Tool pricing
Cost & credit usage across tools
Beyond technical behavior, cost structure shapes how these agents are used. Below is what we observed during this benchmark.
- Roo Code (with OpenRouter) consumed $53.14 in usage.
- Replit consumed $55.04 during execution.
- Windsurf used 256 credits, which is roughly half of its $15 monthly plan allocation (500 credits). Windsurf also allows you to purchase 250 credits for $10.
- Cursor consumed $27.90, which was covered within our $20 membership tier through its included usage model.
- Kiro used 136 credits, which are covered under our $20 membership plan that includes 1000 monthly credits. In Kiro’s pay-to-use model, 100 credits cost $4.
- Antigravity is currently completely free during its public preview.
Methodology
We evaluated AI Code Editors under a one-shot execution setup to measure their autonomous capabilities without human intervention. Agents were then evaluated using our backend and frontend smoke tests to measure infrastructure readiness and behavioral correctness.
Scores reflect:
- Whether the agent produced a runnable system.
- How many backend requirements passed validation.
- How many frontend behaviors were correct.
- Overall reliability across tasks.
The goal was to measure autonomous orchestration, not assisted debugging.
Model configuration
We aimed to use Claude Opus 4.6, as it is one of the strongest models available across most of the editors tested. However, model selection is not uniformly configurable across tools. Replit does not allow model selection.
Each agent was evaluated using its default configuration. We did not tune temperature, retry policies, or reasoning parameters. No optimization or prompt engineering was applied per tool.
This ensures the benchmark reflects how these editors behave out of the box.
Our evaluation goal was to separate and measure:
- Autonomous orchestration reliability
- Build ability (can the agent produce runnable code?)
- Backend behavior correctness
- Frontend behavior correctness
Editor Versions (Late February, 2026)
- Cursor 2.5.25
- Kiro: 0.10.32
- Antigravity: 1.18.4
- Roo-code: 3.50.0
- Replit: February 20, 2026
- Windsurf: 1.9552.25
For evaluation methodology, visit AI Coding Benchmark Methodology.
FAQ
Improved coding efficiency: Automate repetitive tasks and provide intelligent code suggestions.
Enhanced coding experience: Provide a more intuitive and user-friendly coding experience.
Reduced errors: Detect and fix errors in the code.
Increased productivity: Help developers complete tasks faster.
Consider the programming languages supported by the AI code editor.
Look for AI code editors that integrate with existing workflows and tools.
Evaluate the user interface and user experience of the AI code editor. For example, the Cursor and Windsurf editor work as visual studio code forks.
Consider the pricing and availability of the AI code editor.
AI code editors can help developers complete tasks faster and more efficiently in:
– Web development
– Mobile app development
– Enterprise software development
An AI app builder is a platform that uses artificial intelligence to help users create mobile apps without coding.
It automates the development process, allowing users to focus on designing and customizing their apps.
AI app builders can interpret natural language prompts and generate code to build the app. By working as an AI pair programmer, these tools can help a solo developer write new code and problem-solve for an up-to-date codebase.
If you do not need an agentic AI app builder, AI coding assistants like GitHub Copilot and Google Gemini can help you speed up your coding process.
Faster development process with automated coding.
Lower barrier to entry for development, making it accessible to non-technical users.
Cost-effective solution for building mobile apps.
Allows for more freedom in designing and customizing the app for entry-level developers.
It is helpful for businesses that need to build multiple apps quickly.
More on AI coding:
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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