With deadlines getting tighter and codebases growing, we’re all looking for ways to work smarter. AI assistants promise a lot, but it can be hard to sift through the hype. I spent some time working with Gemini, Windsurf and Cursor to see which ones actually make a difference in a real workflow.

Testing Environment

• Integrated Development Environments (IDEs): Windsurf, IntelliJ IDEA Ultimate 2024.1 and Cursor 1.1.3.
• Languages: Java 17, Python 3.11, Terraform 1.5+ and JavaScript/TypeScript.
• Models: Default configurations (Gemini Pro, Claude 3.5 Sonnet and GPT-4).
• Project Sizes: Small (one to 10 files), Medium (50 to 200 files) and Large (300+ files).
• Testing Timelines: June and July 2025.
• Testing Period: 1.5 months of daily usage across multiple projects.

Gemini

Platform: Web-based, Google Workspace integrationModel: Gemini Pro (1.5 million context window)

Pros:

• Documentation Excellence: 95% accuracy in technical documentation generation and 40% faster documentation creation compared to manual writing.
• Learning and Tutoring: Excellent for learning new theoretical concepts and unfamiliar topics, effectively tutors and explains complex ideas in a comprehensible manner.
• Google Workspace Integration: Conveniently creates Google Docs directly from prompt outputs, which is my favorite use case for Gemini — making it ideal for documentation purposes.
• Error Detection: Useful for identifying potential errors or suggesting corrections in ideas and core logic of the code given enough context, but not always accurate.

Cons:

• Code Context Limitations: Inconsistent accuracy in coding-related contexts and struggles with providing precise code-level guidance compared to other tools.
• Project Awareness: Very limited contextual awareness in larger codebases or complex project structures. It’s like a guru you know is there to help explain your legacy code in case nothing works out.
• No IDE Integration: Cannot directly modify code files or run terminal commands.
• Token Limitations: 1.5 million context window but no persistent project memory.

Best For: Documentation generation, learning new concepts, code review, research and evangelizing hypotheses.

Windsurf

Platform: Native desktop application (Electron-based)Model: Claude 3.5 Sonnet with custom fine-tuning

Pros:

• Cascade Agent: The built-in tool, Cascade agent, has superior workflows that are beneficial for writing, documenting and understanding code. It writes to your codebase if you allow it to but can also explain code blocks better than Gemini — obviously because it has way more context because of codebase indexing.
• JetBrains Integration: My favorite part about Windsurf is the JetBrains plugin. It integrates well into IntelliJ or PyCharm and provides the same agentic workflows like the Windsurf editor within your preferred IDE, so that your time is not wasted having to adjust to newer shortcuts and UI.
• Context Preservation: It saves time by keeping workflows within the environment and IDE that you want, minimizing context switching. So, if you’re too much down the JetBrains rabbit hole because of your huge one-language project and you can’t even think of opening it in a Visual Studio Code (VS Code) window ever, I assure you Windsurf will be a way better choice than Copilot or Gemini.
• Advanced Code Analysis: Semantic understanding of code patterns, dependency relationships and architectural decisions.
• Supercomplete: Advanced autocomplete that understands context across multiple workspace files — like a superpowered autocomplete for writing code.
• Turbo Mode: Auto-execution of terminal commands with guardrails, making routine setup tasks much easier.

Cons:

• File Creation Issues: It has a high failure rate in file creation (estimated 25% to 30%), sometimes leading to incomplete or erroneous class generation. So, sometimes even after explaining your problem you will just have to copy the code it gave and place it at the right place, which can be annoying sometimes.
• Performance Bottlenecks: Tab completion is extremely slow and you have to wait a lot sometimes for Windsurf to complete what you can see is evident for it to complete.
• Code Placement Errors: It often misplaces code despite receiving proper context, affecting project structure integrity. Also, code replacement (like refactoring) has some bugs where it does not remove the old code you told it to refactor but just appends it to your file so now you have to remove the old code manually. Slightly bad user experience.
• Interface Quirks: Some interface issues and technical quirks that make it less intuitive for non-developers.

Best For: Large Java/Spring projects, legacy code analysis and enterprise development.

Cursor

Platform: VSCode-based with custom AI extensionsModel: GPT-4 with Claude 3.5 Sonnet fallback

Pros:

• Agent Workflow Excellence: The fastest and most effective agent workflow for code generation and problem-solving. It continuously evaluates and corrects its own approach, minimizing developer intervention.
• Multiple Completion Facilities: Multiple completion facilities, tab completion for basic evident use cases, inline chat to add code or explain some functionality and an agentic workflow (like Cascade for Windsurf above) in the left pane.

• Advanced Agentic Capabilities: The agentic workflow is too good, and in my personal opinion the most seamless tool I have used to date — more helpful than Cascade.

  • Capable of running terminal commands autonomously to resolve encountered issues.
  • Indexing plain English requirements or pseudocode to context-rich suggestions and completions in your file.
  • Creates files, writes tests and comments after understanding the project structure. You can also pass it requirement docs from a new tool and the agent will index the doc for you to write relevant code.
• Custom Rules Engine: Cursor allows addition of workspace-specific (personal rules) and project-specific rules like this for example (which I use for Spring) to provide production-ready suggestions with proper logging, unit testing and comments for functions and classes.
• Multi-Language Support: For Python use cases, as I’m generally not a fan of PyCharm, I usually always open Cursor and delegate tasks to the agent instead. Hence, if I’m working on a small bash, terraform or JS file, opening it in Cursor and getting all my problems solved has always been the go-to approach for me.
• Performance: Fastest response times and highest accuracy rates among all tools tested.
• Ambiguous Prompt Handling: Operates more effectively with ambiguous AI prompts, which is a real power move for non-developers.
• Flexible Terminal Integration: Offers the flexibility to enter terminal commands yourself or run them from chat.

Cons:

• VS Code Limitations: The VS Code UI experience is suboptimal for large Gradle/Maven projects, leading to workflow fragmentation and a continuous feeling of needing to open IntelliJ and thinking, “Maybe Cascade will be able to help me, let’s just change the IDE.”
• Java Ecosystem: Limited support for advanced Java features compared to IntelliJ.
• Learning Curve: Requires understanding of the VS Code ecosystem for optimal usage.
• However, to make my project feel more homely with the JetBrains environment I followed these steps which helped boost productivity a lot as compared to the vanilla VS Code environment in Cursor.

Best For: Rapid prototyping, multi-language projects, small to medium projects and individual developers.

Detailed Task Comparison

I gave Windsurf and Gemini the task of setting null values in data types for scribe event publishing.

• Windsurf: Took about 45 seconds, and it got about 60% of the way there, but it missed some crucial type safety considerations.

Cursor: On the other hand, nailed it in 30 seconds with 95% accuracy. It even flagged type safety issues and suggested a wrapper class. Pretty impressive!

When tasked with creating a utility class for string replacement:

• Windsurf: Struggled, failing after 60 seconds due to a file creation error.
• Cursor: Aced it in just 40 seconds, delivering a complete and accurate implementation with helpful explanations.

Tool Selection Matrix

By Development Phase

• Planning/Design: Gemini (architecture docs, requirements).
• Development: Cursor (code generation, refactoring).
• Testing: Cursor (test generation, debugging).
• Documentation: Gemini (API docs, user guides).
• Maintenance: Windsurf (legacy code understanding).

User Experience Comparison

Conclusion & Recommendations

If You're Flying Solo (Individual Developers):

• Cursor is your best friend for getting things done with smart agent workflows and fixing those pesky errors.
• Gemini shines when you need to learn new stuff or get your documentation in order.

For the Big Players (Enterprise Teams):

• Windsurf is perfect for those massive Java/Spring projects, especially if you live in IntelliJ.
• Cursor is the way to go for teams juggling multiple languages and needing to move fast.

For the Behind-the-Scenes Folks (DevOps/Infrastructure Teams):

• Cursor offers top-notch support for all things Terraform, Docker and Kubernetes.
• Gemini is a whiz at creating clear infrastructure documentation.

Additional Tools:

• GitHub Copilot has an agent mode available in VSCode, slightly more useful than Windsurf but not better than Cursor as of the date of testing.
• Junie is also a useful smart agentic tool developed by Jetbrains.

Success Metrics:

• Time for the first successful code generation.
• Reduction in manual debugging time.
• Documentation completeness and accuracy.
• Team adoption rate and satisfaction scores.