Choosing the Right AI IDE for Your Team: Cursor vs. Windsurf vs. Copilot

tl;dr Over the weekend, I put Cursor, Windsurf, and Copilot (in VS Code) to the test using GPT-5. Tested in both greenfield (starting a project, like our landscape, from scratch) and brownfield scenarios (working with the existing codebase).We leaned into a spec-first development approach (more about this here).All 3 IDEs got the job done. The real differences show up in workflow ergonomics, UI polish, and how much hand-holding each agent needs.My recommendation is to play with these IDEs yourself and pick your own winner based on your taste and team norms.Think of this as a field journal from a dev, not a lab benchmark (test window: Aug 22–24, 2025). Over the weekend, I put Cursor, Windsurf, and Copilot (in VS Code) to the test using GPT-5. Tested in both greenfield (starting a project, like our landscape, from scratch) and brownfield scenarios (working with the existing codebase). landscape We leaned into a spec-first development approach (more about this here). here All 3 IDEs got the job done. The real differences show up in workflow ergonomics, UI polish, and how much hand-holding each agent needs. My recommendation is to play with these IDEs yourself and pick your own winner based on your taste and team norms. Think of this as a field journal from a dev, not a lab benchmark (test window: Aug 22–24, 2025). Pricing at a glance Plan CursorWindsurfCopilot (VS Code) Free2-week trial; includes leading models Infinite completions; 25 chat requests; includes leading models 50 requests; leading models not included Pro$20 monthly → metered (≈$20 of API/agent usage). $200 tier gives ~20× usage across OpenAI/Claude/Gemini $15 monthly → ≈500 prompts on leading models $10 → 300 premium req (includes leadings models); $39 → 1500 premium req Model Notes Access to OpenAI/Claude/Gemini on higher tier At test time, no major Anthropic models Uses GitHub model routing; neat model comparison view Plan CursorWindsurfCopilot (VS Code) Free2-week trial; includes leading models Infinite completions; 25 chat requests; includes leading models 50 requests; leading models not included Pro$20 monthly → metered (≈$20 of API/agent usage). $200 tier gives ~20× usage across OpenAI/Claude/Gemini $15 monthly → ≈500 prompts on leading models $10 → 300 premium req (includes leadings models); $39 → 1500 premium req Model Notes Access to OpenAI/Claude/Gemini on higher tier At test time, no major Anthropic models Uses GitHub model routing; neat model comparison view Plan CursorWindsurfCopilot (VS Code) Free2-week trial; includes leading models Infinite completions; 25 chat requests; includes leading models 50 requests; leading models not included Plan CursorWindsurfCopilot (VS Code) Plan Cursor Windsurf Copilot (VS Code) Free2-week trial; includes leading models Infinite completions; 25 chat requests; includes leading models 50 requests; leading models not included Free 2-week trial; includes leading models Infinite completions; 25 chat requests; includes leading models 50 requests; leading models not included Pro$20 monthly → metered (≈$20 of API/agent usage). $200 tier gives ~20× usage across OpenAI/Claude/Gemini $15 monthly → ≈500 prompts on leading models $10 → 300 premium req (includes leadings models); $39 → 1500 premium req Model Notes Access to OpenAI/Claude/Gemini on higher tier At test time, no major Anthropic models Uses GitHub model routing; neat model comparison view Pro$20 monthly → metered (≈$20 of API/agent usage). $200 tier gives ~20× usage across OpenAI/Claude/Gemini $15 monthly → ≈500 prompts on leading models $10 → 300 premium req (includes leadings models); $39 → 1500 premium req Pro $20 monthly → metered (≈$20 of API/agent usage). $200 tier gives ~20× usage across OpenAI/Claude/Gemini $15 monthly → ≈500 prompts on leading models $10 → 300 premium req (includes leadings models); $39 → 1500 premium req Model Notes Access to OpenAI/Claude/Gemini on higher tier At test time, no major Anthropic models Uses GitHub model routing; neat model comparison view Model Notes Access to OpenAI/Claude/Gemini on higher tier At test time, no major Anthropic models Uses GitHub model routing; neat model comparison view Greenfield exploration: Build a project from scratch with a spec I've prompted each tool to leverage this greenfield-spec.md (describing architecture, basic MERN stack scaffold) and then asked it to implement the spec, iterate, and adjust tests. In my observations, all specs produced by all three were very similar and all delivered working code. greenfield-spec.md On the UI/UX experience, Cursor felt the most professional in flow/explanations, but once refused to auto-build the project from spec. When it did build, it created a clean structure + tests and adjusted correctly as I changed the spec. I also appreciated how Cursor usefully shows % of context consumed (~400k token total context with 272k input + 128k output). As for inline speed: Cursor ≈ Copilot > Windsurf. Though fast, Copilot's in-line refactoring fell short to Windsurf and Cursor's output. ~400k 272k input + 128k output Cursor ≈ Copilot > Windsurf When running the spec, Windsurf went full send. It created the whole project structure automatically (folders/files) without extra prompting. Windsurf's chat pane made it easy to distinguish model "thinking", command runs, and issues. Copilot (VS Code) mostly showed file contents in chat with path hints, but didn't immediately write an on-disk tree. I did appreciate Copilot's in-IDE browser preview though, which was delightful for quick checks. For testing, Windsurf and Cursor's generated tests passed on the first run in my sample. Copilot authored the strongest tests with granular mocking boundaries and edge case coverage. Though I had to wrestle a bit to get them passing, the self-contained modules made failures immediately traceable to specific component behaviors. Brownfield exploration: extend a legacy codebase Getting started: Upon running locally, Copilot got the server up first, followed by Windsurf second and Cursor third - though notably, Cursor created a new .env.local instead of finding the right one, which slowed me down. Getting started .env.local Codebase explanation: When it came to understanding the existing code, Windsurf explained the codebase exceptionally well, and the play on response format/highlights made it the best experience of the 3. That said, Cursor and Copilot were also able to read and explain legacy code effectively. Codebase explanation New feature (asked all to add a tool detail page + schema comparison across tools): All three successfully built from the brownfield-spec.md and produced sensible tests that ran well. This reinforced an important lesson: the clearer your spec, the more concrete the result. New feature brownfield-spec.md Bug fix (subtle PostHog lazy-init bug): Here, Windsurf and Cursor diagnosed it faster, while Copilot missed it in my run. It's important to highlight that the bug comments were not added in the file (that would have been too easy 🙃). Bug fix PostHog lazy-init bug Refactor (scripted request across multiple files): All 3 executed the steps and staged changes properly, though with different approaches. Copilot required more approvals (e.g., 7 terminal prompts vs. Windsurf's 3 and Cursor's 1), which is great for caution but slower for flow. Interestingly, Copilot would sometimes say it's "been working on this problem for a while" and stall, whereas the others tended to keep iterating until done. Refactor scripted request UI & DX details that matter Overall, Windsurf > Cursor > Copilot for terminal/chat integration. Copilot runs commands outside the chat pane, which unfortunately breaks the narrative thread for me. Meanwhile, Cursor's default look and progress indicators feel the most cohesive, Windsurf communicates what's happening the best, and Copilot nails browser-in-IDE and markdown rendering. Windsurf > Cursor > Copilot On context and memory: Windsurf's "just remembers" feel is particularly strong for memory and context retention. Cursor gets there with rules/notes but will lose thread in long sessions, while Copilot keeps things simpler but consequently more ephemeral. On context and memory: Notable touches: Notable touches: Windsurf can continue proposing changes in the background while you review staged diffs - a nice touch for maintaining flow.Cursor's multi-file edit flow is strong, though I'd love a nudge when I forget I'm in "Ask" vs. "Agent" mode.Copilot's "Ask" mode sometimes defaults to doing rather than explaining; additionally, starting a new chat ends the current edit session.I hit one infinite loop with Copilot (malformed grep); a restart fixed it → but it made me wonder: could this have been resolved without my intervention?" Windsurf can continue proposing changes in the background while you review staged diffs - a nice touch for maintaining flow. Cursor's multi-file edit flow is strong, though I'd love a nudge when I forget I'm in "Ask" vs. "Agent" mode. Copilot's "Ask" mode sometimes defaults to doing rather than explaining; additionally, starting a new chat ends the current edit session. I hit one infinite loop with Copilot (malformed grep); a restart fixed it → but it made me wonder: could this have been resolved without my intervention?" grep Implementation Results, Key Takeaways. Area CursorWindsurf Copilot (VS Code) Greenfield build Slick flow; solid structure + tests; once refused auto-build from spec; fastest inline suggestions Auto-creates full project structure without asking; a tad slower inline; very clear terminal/steps UI Shows file contents in chat (not a physical tree); browser panel inside IDE is great Brownfield (legacy repo)Explained codebase clearly; struggled with .env.local → server late to run Strong codebase explainer; second to run server First to run server locally Tests Good structure; passed on first try in my runs Good; clear logs; steady Strongest test focus, but took longer to make them pass initially Agent behavior Feels “aware” of when you want to build vs. converse; good multi-file edits “Cascade” style workflow; keeps context/memory better; keeps working while you review Tends to “do things” even for simple Q&A; more human-in-the-loop approvals Context & memory Explicit rules/files help; shows context usage % Best seamless memory/continuity in practice Session model simpler; starting a new chat ends current edit session Chat/Terminal UI Polished; good progress plan UI Best separation of agent thoughts, commands, highlights Terminal not inside chat; markdown paste turns into beautiful request formatting Speed (inline) FastestSlightly slower Similar to Cursor Frustrations-Inline could be faster no possibility of multiple chats, no terminal in-chat, in-line sometimes struggles to capture intent Neat bits shows % of token limit; custom retrievals Smart UI hints and file logos in chat; background work while you review In-IDE browser; pretty markdown once pasted in request Area CursorWindsurf Copilot (VS Code) Greenfield build Slick flow; solid structure + tests; once refused auto-build from spec; fastest inline suggestions Auto-creates full project structure without asking; a tad slower inline; very clear terminal/steps UI Shows file contents in chat (not a physical tree); browser panel inside IDE is great Brownfield (legacy repo)Explained codebase clearly; struggled with .env.local → server late to run Strong codebase explainer; second to run server First to run server locally Tests Good structure; passed on first try in my runs Good; clear logs; steady Strongest test focus, but took longer to make them pass initially Agent behavior Feels “aware” of when you want to build vs. converse; good multi-file edits “Cascade” style workflow; keeps context/memory better; keeps working while you review Tends to “do things” even for simple Q&A; more human-in-the-loop approvals Context & memory Explicit rules/files help; shows context usage % Best seamless memory/continuity in practice Session model simpler; starting a new chat ends current edit session Chat/Terminal UI Polished; good progress plan UI Best separation of agent thoughts, commands, highlights Terminal not inside chat; markdown paste turns into beautiful request formatting Speed (inline) FastestSlightly slower Similar to Cursor Frustrations-Inline could be faster no possibility of multiple chats, no terminal in-chat, in-line sometimes struggles to capture intent Neat bits shows % of token limit; custom retrievals Smart UI hints and file logos in chat; background work while you review In-IDE browser; pretty markdown once pasted in request Area CursorWindsurf Copilot (VS Code) Greenfield build Slick flow; solid structure + tests; once refused auto-build from spec; fastest inline suggestions Auto-creates full project structure without asking; a tad slower inline; very clear terminal/steps UI Shows file contents in chat (not a physical tree); browser panel inside IDE is great Area CursorWindsurf Copilot (VS Code) Area Cursor Windsurf Copilot (VS Code) Greenfield build Slick flow; solid structure + tests; once refused auto-build from spec; fastest inline suggestions Auto-creates full project structure without asking; a tad slower inline; very clear terminal/steps UI Shows file contents in chat (not a physical tree); browser panel inside IDE is great Greenfield build Slick flow; solid structure + tests; once refused auto-build from spec; fastest inline suggestions Auto-creates full project structure without asking; a tad slower inline; very clear terminal/steps UI Shows file contents in chat (not a physical tree); browser panel inside IDE is great Brownfield (legacy repo)Explained codebase clearly; struggled with .env.local → server late to run Strong codebase explainer; second to run server First to run server locally Tests Good structure; passed on first try in my runs Good; clear logs; steady Strongest test focus, but took longer to make them pass initially Brownfield (legacy repo)Explained codebase clearly; struggled with .env.local → server late to run Strong codebase explainer; second to run server First to run server locally Brownfield (legacy repo) Explained codebase clearly; struggled with .env.local → server late to run Strong codebase explainer; second to run server First to run server locally Tests Good structure; passed on first try in my runs Good; clear logs; steady Strongest test focus, but took longer to make them pass initially Tests Good structure; passed on first try in my runs Good; clear logs; steady Strongest test focus, but took longer to make them pass initially Agent behavior Feels “aware” of when you want to build vs. converse; good multi-file edits “Cascade” style workflow; keeps context/memory better; keeps working while you review Tends to “do things” even for simple Q&A; more human-in-the-loop approvals Context & memory Explicit rules/files help; shows context usage % Best seamless memory/continuity in practice Session model simpler; starting a new chat ends current edit session Chat/Terminal UI Polished; good progress plan UI Best separation of agent thoughts, commands, highlights Terminal not inside chat; markdown paste turns into beautiful request formatting Speed (inline) FastestSlightly slower Similar to Cursor Frustrations-Inline could be faster no possibility of multiple chats, no terminal in-chat, in-line sometimes struggles to capture intent Neat bits shows % of token limit; custom retrievals Smart UI hints and file logos in chat; background work while you review In-IDE browser; pretty markdown once pasted in request Agent behavior Feels “aware” of when you want to build vs. converse; good multi-file edits “Cascade” style workflow; keeps context/memory better; keeps working while you review Tends to “do things” even for simple Q&A; more human-in-the-loop approvals Agent behavior Feels “aware” of when you want to build vs. converse; good multi-file edits “Cascade” style workflow; keeps context/memory better; keeps working while you review Tends to “do things” even for simple Q&A; more human-in-the-loop approvals Context & memory Explicit rules/files help; shows context usage % Best seamless memory/continuity in practice Session model simpler; starting a new chat ends current edit session Context & memory Explicit rules/files help; shows context usage % Best seamless memory/continuity in practice Session model simpler; starting a new chat ends current edit session Chat/Terminal UI Polished; good progress plan UI Best separation of agent thoughts, commands, highlights Terminal not inside chat; markdown paste turns into beautiful request formatting Chat/Terminal UI Polished; good progress plan UI Best separation of agent thoughts, commands, highlights Terminal not inside chat; markdown paste turns into beautiful request formatting Speed (inline) FastestSlightly slower Similar to Cursor Speed (inline) Fastest Slightly slower Similar to Cursor Frustrations-Inline could be faster no possibility of multiple chats, no terminal in-chat, in-line sometimes struggles to capture intent Frustrations - Inline could be faster no possibility of multiple chats, no terminal in-chat, in-line sometimes struggles to capture intent Neat bits shows % of token limit; custom retrievals Smart UI hints and file logos in chat; background work while you review In-IDE browser; pretty markdown once pasted in request Neat bits shows % of token limit; custom retrievals Smart UI hints and file logos in chat; background work while you review In-IDE browser; pretty markdown once pasted in request So… who should use what? All three experiences ride on VS Code - Windsurf and Cursor are forks; Copilot runs inside VS Code. That means capability deltas may be subtle: same keyboards, similar models, similar APIs. The differentiation shows up in micro-interactions, like how the agent plans, how much it explains, how the terminal/chat loop flows, and how well the UI helps you stay oriented. If you're expecting a jaw-dropping, orders-of-magnitude difference, that wasn't my experience. In my opinion, the small product details and moments of delight are what choose your daily driver. That said, I found that each IDE may come in use depending on your use case. This is a lightly opinionated take, not gospel: Cursor → You care about polish, tight multi-file edits, and a feeling that the agent understands when to execute vs. chat. Great for folks who want structure and speed in daily flow. This may work best for senior/full-stack IC at a startup or small team. Windsurf → You value context retention and a workflow-first UI that narrates what's happening. Strong pick for larger codebases where "not losing the thread" is key. I can see a staff, Principal engineer, or maintainer working in a large/long-lived repo leveraging Windsurf. Copilot (VS Code) → You want trust and more human-in-the-loop vibe. Company teams already in the Microsoft/GitHub ecosystem will feel right at home. I think this is a fit for team lead or IC in GitHub-standardized orgs, who prefer trusted default and governance. Cursor → You care about polish, tight multi-file edits, and a feeling that the agent understands when to execute vs. chat. Great for folks who want structure and speed in daily flow. This may work best for senior/full-stack IC at a startup or small team. Cursor Windsurf → You value context retention and a workflow-first UI that narrates what's happening. Strong pick for larger codebases where "not losing the thread" is key. I can see a staff, Principal engineer, or maintainer working in a large/long-lived repo leveraging Windsurf. Windsurf Copilot (VS Code) → You want trust and more human-in-the-loop vibe. Company teams already in the Microsoft/GitHub ecosystem will feel right at home. I think this is a fit for team lead or IC in GitHub-standardized orgs, who prefer trusted default and governance. Copilot (VS Code) It's important to note that this exercise comes with several limitations, including (and not limited to) the complexity of the builds, the tests coverage, the size of the codebase, etc. That's why I recommend experimenting with these tools yourself: you'll want to choose the one that best matches your (or your team's) needs. Finally, the current status quo is only ephemeral. Each IDE may develop defensibility with how fast they ship their features. Cursor's own in-line edits as an example has been developed in-house, and was clearly felt throughout this experiment. It will be interesting to observe the pace of feature releases from these three tools. When I converse with engineers, the consensus leans toward Cursor demonstrating stronger execution capabilities, while Copilot appears to be slower in comparison. Meanwhile, given the recent tumultuous Windsurf acqui-hire and the accompanying leadership changes, we may see a shift in its release cadence. Closing thoughts: A missing spec-driven feature Despite their capabilities, none of these IDEs fully embrace spec-driven development as a first-class citizen (unlike the recent release of Kiro). Kiro While they can generate and work from specs, the workflow remains bolted-on rather than native - you're still manually adding your specs, losing track of which code implements which requirements, and lacking formal verification that implementations match specifications. Memory and context management, though improving, remains ad hoc across all three; they remember recent edits but struggle to maintain architectural decisions or cross-session constraints. Test and mock generation quality varies wildly depending on how you prompt, with no systematic approach to coverage or edge cases. The gaps become even more glaring: no audit trails for AI-generated code changes, absent cost attribution per dev or project, and no integration with existing governance workflows. These tools lack team-wide consistency enforcement - each developer's AI assistant learns different patterns, creating style drift across the codebase. There's no way to enforce company-specific architectural patterns, security requirements, or compliance rules at the AI level. This points to non trivial opportunity for next-gen IDEs where specs aren't just documentation but executable contracts, where AI agents understand and preserve organizational standards, and where generated code comes with provenance and accountability. If you are interested in learning more about the interesting projects built around spec-driven development, you can search find them on the Landscape, the guide to the AI development ecosystem. Landscape, the guide to the AI development ecosystem.