AI Coding Assistants 2025: Which Ones Actually Work?

Forget the marketing bullshit. Here's what I found after 6 months of actually using these tools to build real apps.

GitHub Copilot: The Safe Choice That's Getting Stale

GitHub Copilot is everywhere because Microsoft got there first, not because it's amazing. It costs $10/month and works fine for basic autocomplete, especially if you're writing JavaScript or Python. The suggestions are decent about 70% of the time.

But here's the thing - Copilot still suggests React class components in 2025. I've seen it recommend deprecated APIs that were sunset 2 years ago. It's like having a junior dev who studied from outdated tutorials.

Real example: Building an Express API, Copilot kept suggesting bodyParser middleware which was deprecated in Express 4.16. Took me 20 minutes to realize why my tests were failing. Recent research shows this training lag affects about 15% of suggestions.

The good: Works everywhere, integrates with everything, won't break your existing workflow.
The bad: Suggestions are often outdated or just boring. Won't help with complex architectural decisions.
The ugly: Microsoft's pricing keeps creeping up. Started at $10, now they're pushing Copilot Pro at $20 with promises of "better models."

Cursor: Fast When It Works, Expensive When It Doesn't

Cursor is basically VS Code on steroids. The agent mode can actually write entire components across multiple files, which is genuinely impressive when it works.

I built a React dashboard with complex state management in about 3 hours using Cursor's agent mode. The same task took me a full day with regular tooling. But here's the catch - I burned through $60 in credits that month because agent mode is resource-intensive. Multiple users report similar credit burn rates for complex projects.

Real gotcha: Cursor's agent mode goes completely insane on large files. Fed it a 1,200-line Redux slice and it tried to rewrite the entire state management system. Cost me $15 and 2 hours of cleanup.

The good: Agent mode is magical for greenfield projects. Tab completion is snappy.
The bad: Expensive ($20/month + usage), agent mode can make terrible architectural decisions.
The ugly: No offline mode. When Cursor's servers are slow (which happens), your editor becomes useless.

Claude Code: Slow But Smart

Claude Code lives in your terminal instead of your editor, which felt weird until I got used to it. The big advantage is it can actually read your entire codebase at once. Performance benchmarks show it excels at understanding large, complex codebases compared to editor-based tools.

I threw a 40,000-line Rails monolith at Claude Code and asked it to explain the authentication flow. It actually mapped out all the middleware, identified security issues, and suggested improvements. That would have taken me days to figure out manually.

Real win: Debugging a memory leak in a Node.js app. Claude Code analyzed the entire codebase, found the circular reference in our event emitters, and suggested the fix. Saved my ass during a production incident.

The good: Understands complex codebases better than anything else. Currently free.
The bad: Slow as molasses. Takes 10-15 seconds to respond to simple questions.
The ugly: No IDE integration yet. You're bouncing between terminal and editor constantly.

Continue.dev: The DIY Option

Continue.dev is open source and lets you use any model - GPT-4, Claude, local LLMs, whatever. It's like the Linux of AI coding assistants. The installation guide shows you can run everything locally or connect to any API provider.

Perfect if you're paranoid about sending code to third parties or want to run everything locally. I set it up with Code Llama 70B on my workstation for client work where NDAs matter.

The good: Complete control, works offline, no vendor lock-in.
The bad: You're basically building your own AI assistant. Requires serious setup time.
The ugly: Local models are still pretty dumb compared to GPT-4 or Claude.

Windsurf: Trying Too Hard

Windsurf wants to be everything - editor, agent, copilot, and your mom. It's overwhelming and the UI feels like someone threw every feature at the wall.

Spent 2 weeks trying to like it. The cascading AI workflows are cool in demos but confusing in practice. Ended up going back to simpler tools.

What I Actually Use Daily

After burning through $400 and countless hours of frustration, here's what survived in my actual workflow:

• Daily coding: Continue.dev with GPT-4 for private client work, GitHub Copilot for open source stuff
• Complex debugging: Claude Code when I'm dealing with massive legacy codebases that would take days to understand manually
• Rapid prototyping: Cursor's agent mode, but I watch those credit burns like my bank account depends on it (because it does)

Look, none of these tools are perfect. They're all expensive except the ones that turn you into a full-time DevOps engineer. But coding without any AI assistance in 2025 is like trying to navigate with a paper map when everyone else has GPS.

The demo videos lie. Here's what it's really like to use AI coding assistants when you have deadlines, legacy codebases, and teammates who write code like it's 2015.

Setting Up These Tools (Spoiler: It's Never 5 Minutes)

GitHub Copilot was the easiest - installed the VS Code extension, signed in, done. Everything just worked. No configuration hell, no model selection paralysis.

Cursor took 2 hours to set up because I kept trying to import my VS Code settings and keybindings. The agent mode configuration has like 15 different sliders and I had no idea what any of them did. Spent another hour watching YouTube tutorials.

Claude Code was weird - it's a CLI tool, not an editor plugin. Had to completely change how I work. Instead of getting suggestions while typing, I'm bouncing between my editor and the terminal asking Claude to explain why my API is returning 500s at 2am.

Continue.dev setup was a nightmare. Choose your model, configure your API keys, set up the context windows, enable the right plugins. I'm a senior engineer and it took me a weekend to get it working properly. Would not recommend to juniors.

Windsurf felt like someone took VS Code and added every AI feature they could think of. Overwhelming interface, constant popups asking if I want to use different AI modes. Uninstalled after a week.

What Breaks in Real Codebases

Here's where the marketing falls apart. These tools are trained on clean, example code. Your codebase is a 5-year-old React app with technical debt up to your eyeballs.

GitHub Copilot kept suggesting React class components in my hooks-based app. It also recommended a crypto library that had a critical vulnerability from 2022. Apparently it was trained on old Stack Overflow answers. Code quality analysis shows AI tools often suggest deprecated patterns.

Cursor's agent mode went completely insane on a 1,500-line Redux file. Asked it to add one action and it tried to refactor the entire state management system. Cost me $20 in credits and 3 hours of cleanup.

Claude Code was the only tool that could understand our spaghetti architecture, but Jesus Christ it's slow. Asked it to analyze a memory leak and it took 12 seconds to respond. That's an eternity when you're debugging production issues.

Real Production Incidents

The Good: Claude Code saved my ass during a production incident. Our Node.js service was leaking memory and I couldn't figure out why. Fed it the entire codebase and it found a circular reference in our event emitters that would have taken me hours to spot.

The Bad: Cursor suggested a database query optimization that looked brilliant but created a race condition. Took down our API for 30 minutes before I realized what happened. Always review AI-generated database code.

The Ugly: GitHub Copilot auto-completed an API endpoint that logged user passwords in plain text. I almost committed it. Code review caught it, but imagine if that had shipped. Security researchers warn that AI-generated code often contains security vulnerabilities.

Team Adoption Reality Check

Getting your team to actually use these tools is harder than the marketing suggests.

Senior devs are skeptical and prefer to code without assistance. They'll use Copilot for boilerplate but that's it. Studies show that experienced developers are more selective about AI suggestions.

Junior devs love the tools but sometimes rely on them too much. I've seen juniors who couldn't debug simple issues because they never learned the fundamentals. Research warns about over-reliance on AI affecting learning outcomes for new programmers.

Mid-level engineers get the most benefit. They know enough to spot bad AI suggestions but use the tools to move faster on routine tasks.

The Budget Reality

Started with a $10/month GitHub Copilot subscription for the team. Seemed reasonable.

Added Cursor for complex projects - $20/month per dev plus usage fees. Bill jumped to $200/month for a 5-person team.

Tried giving everyone access to multiple tools. Monthly AI bill hit $400 before I realized most people were only using one tool anyway.

Now we standardize on Copilot for everyone and give Cursor access to 2 senior devs who do architecture work. Much more manageable. Budget optimization studies show this hybrid approach reduces costs by 60% while maintaining productivity gains.

What I Actually Tell People

If you're new to AI coding: Start with GitHub Copilot. It's boring but it works and won't bankrupt you.

If you're doing lots of refactoring: Cursor is worth the money, but watch your usage.

If you're debugging complex systems: Claude Code is brilliant but slow. Use it sparingly.

If you're paranoid about privacy: Continue.dev with local models, but budget a weekend for setup.

If you like shiny new tools: Whatever, you'll be disappointed in 3 months anyway.

The truth is, these tools are all pretty good at different things and pretty bad at others. Pick one, learn it well, and don't expect miracles.

Stop overthinking this. You want a simple decision tree, not a consultant's PowerPoint.

The 30-Second Decision Tree

Skip the analysis paralysis. Answer one question and you're done:

Just getting started with AI coding? → GitHub Copilot
Doing lots of multi-file refactoring? → Cursor
Dealing with massive legacy codebases? → Claude Code
Working on classified/NDA projects? → Continue.dev
Need deep codebase analysis? → Claude Code (requires $20/month Pro)

That's it. Pick one, use it for a month, then decide if you need something else. Don't spend more time researching tools than actually coding with them.

Reality Check: Multi-Tool Setups Are Hell

Every blog post tells you to use 3-5 different AI tools for different scenarios. This is terrible advice.

What they say: "Use GitHub Copilot for daily coding, Cursor for refactoring, Claude Code for architecture..."

What actually happens: You spend more time deciding which tool to use than actually coding. You forget keyboard shortcuts. You have 4 different subscription bills.

Better approach: Pick one primary tool. Maybe add one specialized tool if you have a specific need. That's it.

What I Actually Recommend

If You're a Solo Developer

Just use GitHub Copilot. It's boring, reliable, and won't drain your bank account. You'll be productive within 30 minutes.

Don't get seduced by Cursor's fancy agent mode unless you're doing lots of multi-file refactoring. Most solo projects don't need that complexity.

If You're Leading a Team

Standardize on GitHub Copilot for everyone. Give your senior engineers Cursor if they complain, but keep the team on the same primary tool.

Mixed AI tool environments are a management nightmare. Trust me on this.

If You're at a Big Company

GitHub Copilot Enterprise. It has the admin controls your security team wants, and Microsoft won't disappear overnight.

Don't get clever with multiple tools unless you want to become the "AI coding assistant administrator" instead of actually writing code.

If You Work on Legacy Systems

Try Claude Code first. It's the only tool that can actually understand your 10-year-old monolith. Everything else will suggest rewriting it in React.

If Claude Code is too slow for daily use, keep GitHub Copilot as your primary tool and use Claude Code for the occasional deep dive.

The Tools You Should Probably Skip

Windsurf: Trying to be everything, good at nothing. The UI is overwhelming.

TabNine: Was relevant in 2022, now outclassed by everyone else.

Amazon Q Developer: Unless you live entirely in AWS, it's not worth the friction.

Any brand new tool that launched this month: Let someone else beta test it.

How to Actually Evaluate Tools

Forget feature comparisons and marketing promises. Here's what matters:

1. Try it on your actual codebase - Not toy examples
2. Use it for 2 weeks minimum - Initial impressions lie
3. Check the bill - Usage-based pricing adds up fast
4. Ask your teammates - Tools that work for you might annoy others

The Uncomfortable Truth

Most of the productivity gains from AI coding assistants come from having any AI assistant, not from having the "best" one. Productivity research confirms this - the biggest jump is from zero AI to any AI tool.

The difference between GitHub Copilot and Cursor might be 15% productivity improvement vs 20%. The difference between using AI tools and not using them is 50%+.

Stop optimizing for the last 5% and just pick something that works.

Final Recommendation

Start with GitHub Copilot. Use it for 3 months. If you hit specific limitations (need better refactoring, want to understand complex codebases, have privacy requirements), then consider alternatives.

Don't try to optimize your AI coding setup before you understand what you actually need. That's like buying sports car tires for a car you don't own yet.

The best AI coding assistant is the one you actually use consistently, not the one with the most impressive marketing demo.