Claude vs ChatGPT vs Gemini 2026: Which AI Coding Assistant Actually Saves You Time

Coding assistant comparison

I’ve been writing code professionally for twelve years. I’ve seen frameworks come and go, languages rise and fall, and tools promise to revolutionize development only to fade into obscurity. But AI coding assistants? They’re different. They’re actually changing how I work.

For the past eight months, I’ve been using Claude, ChatGPT, and Gemini daily in my development workflow. Not just testing them—actually relying on them to ship features, debug production issues, and meet deadlines. I’ve spent money on all three, hit their limitations. I’ve also had moments where they saved me hours of work.

This isn’t a theoretical comparison based on benchmarks or marketing materials. This is what actually happened when I used these tools to build real applications, fix real bugs, and solve real problems.

Let me tell you what I learned about which one actually saves you time—and more importantly, when each one is worth using.

My Testing Methodology: Real Work, Real Deadlines

I didn’t create artificial coding tests. I used these AI assistants for my actual work:

The projects:

• A Laravel SaaS application with 47,000 lines of code
• A React dashboard with complex data visualizations
• Python data processing scripts handling 2GB CSV files
• Database migration and optimization work
• API integration with third-party services
• Debugging production issues under pressure

What I tracked:

• Time to get a working solution
• Number of iterations needed
• Quality of initial response
• Accuracy of code suggestions
• Ability to understand context
• Cost per month of actual usage
• Frustration level (yes, I tracked this)

The rules:

• Same problem given to all three
• Timed from first prompt to working code
• Counted revisions and clarifications needed
• Tested edge cases and production readiness
• Measured my actual productivity, not theoretical capabilities

Let me be clear upfront: there’s no perfect winner. Each has strengths. Each has moments where it shines and moments where it fails spectacularly.

Claude Sonnet 4: The Deep Thinker

What I pay: $20/month for Claude Pro Primary use case: Complex debugging, architecture decisions, and explaining messy code

Claude has become my default for anything that requires actual thinking. Not just code generation—thinking.

Where Claude Excels

1. Understanding Context and Nuance

I gave Claude a Laravel controller that was 350 lines long with nested conditionals, three database transactions, and some truly terrible naming conventions (legacy code, not mine, I swear). I asked it to explain what was happening.

Claude didn’t just describe the code. It understood the business logic, identified the anti-patterns, explained why the original developer probably wrote it that way, and suggested a refactor that preserved the existing behavior while making it maintainable.

ChatGPT gave me a line-by-line explanation that was technically correct but missed the forest for the trees. Gemini summarized it but glossed over the complexity.

Real example: I had a production bug where orders were occasionally being double-charged. The payment processing code spanned three files, involved race conditions, and had been touched by four different developers over two years.

I pasted all three files into Claude with the bug description. It immediately identified the race condition, explained exactly why it happened (including the specific scenario where two requests could overlap), and suggested a fix using database locks. The explanation was so clear that I could explain it to my non-technical project manager.

Time saved: Probably 4-6 hours of debugging. The bug was subtle enough that I might not have found it without the clear explanation.

2. Long Context Window That Actually Works

Claude’s 200K token context window isn’t just marketing—it’s genuinely useful. I pasted an entire database schema (34 tables), three related migration files, and asked it to help design a new feature that touched multiple tables.

It kept track of relationships, foreign keys, naming conventions, and even noticed that we were inconsistent with our timestamp naming (some tables used created_at, others used date_created).

I tried the same with ChatGPT and Gemini. ChatGPT seemed to lose track around message 15. Gemini handled it better but occasionally forgot context from earlier in the conversation.

3. Admitting Uncertainty

This might sound weird, but I trust Claude more because it says “I’m not certain” when it’s not certain.

When I asked it about a specific Laravel 11 feature that was in beta, it said: “Based on the Laravel 11 beta documentation, this feature works like X, but since it’s still in beta, the final implementation might differ. I’d recommend checking the official docs for the latest.”

ChatGPT gave me a confident answer that turned out to be outdated. Gemini said it couldn’t find information on beta features.

The difference? Claude’s uncertainty saved me from confidently writing code based on wrong information.

Where Claude Struggles

1. Speed

Claude is noticeably slower than ChatGPT. When I need to generate a quick utility function or convert some JSON to a TypeScript interface, the extra 3-5 seconds matters. It adds up over a day.

For rapid-fire questions like “how do I sort this array descending in JavaScript?” or “what’s the PHP function to get file size?”, Claude feels sluggish.

2. Code Artifacts Can Be Finicky

Claude generates code in these interactive “artifacts” that you can edit. It’s a nice feature—until it isn’t. Sometimes I just want to copy code without dealing with the artifact UI. Sometimes the artifact doesn’t include all the code I need because Claude split it up.

For small snippets, I often find myself just asking for plain text output.

3. Message Limits

Even with Claude Pro, you get rate limited. On busy days where I’m really leaning on it, I’ve hit the limit around 6 PM and had to wait for the reset. This is frustrating when you’re on a deadline.

The limit seems to be based on computational complexity, not just message count, so one complex request can eat a lot of your quota.

4. No Real-Time Information

Claude’s training data has a cutoff date. When I asked about the latest AWS service features or breaking changes in a framework’s newest version, it either has outdated information or says it doesn’t know.

I once spent 30 minutes debugging an issue based on Claude’s suggestion, only to find out the API had changed two weeks prior and Claude didn’t know.

ChatGPT (GPT-4): The Quick Responder

What I pay: $20/month for ChatGPT Plus (though I’m still on GPT-4; GPT-4o access varies) Primary use case: Quick code generation, syntax questions, and rapid prototyping

ChatGPT has been around the longest, and it shows. The tool is polished, fast, and rarely breaks. It’s my go-to when I need an answer right now.

Where ChatGPT Excels

1. Speed

This is ChatGPT’s killer feature. It’s fast. Really fast. Responses start streaming immediately, and most answers are complete within 3-5 seconds.

When I’m in flow state and need quick answers—”how do I format a date in Python?”, “what’s the SQL syntax for this?”, “write me a regex for email validation”—ChatGPT keeps me moving.

I measured my actual usage over a week: ChatGPT answered 64% of my queries in under 5 seconds. Claude took 8-12 seconds on average. That difference is meaningful when you’re asking 30-40 questions a day.

2. Plugins and Web Browsing

ChatGPT Plus includes web browsing and plugins. When I need current information—”what’s the latest version of React and what changed?”—it can search and tell me.

I was working with a new API that had poor documentation. ChatGPT searched their website, found example code in their GitHub issues, and synthesized a working example. Claude would have been working from older information.

The plugins ecosystem is hit-or-miss, but when they work, they’re useful. The code interpreter can run Python code, which helped me debug a data processing script by actually executing it.

3. Consistency

ChatGPT rarely has downtime. It’s been the most reliable of the three. When I need it, it works. The response quality is predictable—not always the best, but consistently decent.

This reliability matters more than it sounds. I’ve had multiple instances where Claude or Gemini had issues right when I needed them. ChatGPT has been rock solid.

4. Better at Following Specific Formats

When I need code in a very specific format—”write this as a TypeScript interface”, “convert this to a Laravel migration”, “make this a JSON schema”—ChatGPT follows instructions more precisely.

I asked all three to convert the same Python function to JavaScript. Claude gave me idiomatic JavaScript but with extra explanations. Gemini gave me JavaScript but with Python-style comments. ChatGPT gave me exactly what I asked for, nothing more.

Where ChatGPT Struggles

1. Context Window Issues

ChatGPT’s context window is smaller, and you feel it. In longer conversations, it starts forgetting important details from earlier messages.

I was working through a complex refactoring across multiple files. By message 20, ChatGPT was suggesting things that contradicted decisions we’d made in messages 5-8. I had to keep reminding it of previous context.

This makes it less useful for marathon debugging sessions or architectural planning.

2. Overconfidence in Wrong Answers

ChatGPT rarely says “I don’t know.” It gives answers with complete confidence, even when wrong.

I asked about a specific Laravel feature interaction. ChatGPT gave me a detailed explanation of how it worked, including code examples. The problem? The feature didn’t exist in the version I was using. It hallucinated the entire thing.

I wasted 45 minutes trying to implement something that wasn’t possible because ChatGPT was so convincing.

3. Verbose Without Adding Value

ChatGPT loves to explain things I already know. When I ask for code, I often get:

• Two paragraphs explaining what the code will do
• The actual code
• Two more paragraphs explaining what I should do next
• A reminder to test it

Sometimes I just want the code. The verbosity slows me down when I need rapid iteration.

4. Generic Solutions

ChatGPT tends toward generic, safe solutions. It works, but it’s not always the best solution for your specific situation.

When I asked for help optimizing a slow database query, ChatGPT suggested standard indexing advice that you’d find in any tutorial. Claude analyzed my specific schema and suggested a covering index with exactly the columns I needed.

Gemini Advanced: The Wildcard

What I pay: $19.99/month for Google One AI Premium (includes 2TB storage) Primary use case: Google Workspace integration and when I need current information

Gemini is the newest major player, and it shows. The potential is there, but it feels less mature than Claude or ChatGPT.

Where Gemini Excels

1. Integration with Google Ecosystem

If you live in Google Workspace, Gemini has real advantages. It can access my Google Drive, Sheets, and Docs.

I had a CSV file with production data in Google Sheets. I asked Gemini to analyze it and write a Python script to process similar files. It could see the actual data structure and generate accurate code based on the real columns and data types.

This is powerful when you need code that works with your specific data.

2. Multimodal Capabilities

Gemini handles images better than the others. I took a screenshot of a design mockup and asked it to write the HTML/CSS. The result was closer to the design than what Claude or ChatGPT produced from descriptions.

I also used it to debug UI issues by showing it screenshots of the broken layout versus what it should look like.

3. Genuinely Impressive at Times

There are moments where Gemini just gets it in a way that surprises me. It’s hard to quantify, but occasionally it makes connections or suggestions that feel innovative rather than derivative.

When brainstorming architecture for a new feature, Gemini suggested an approach I hadn’t considered that ended up being simpler than what I was planning.

4. Current Information Built In

Gemini seems to have more recent training data or better access to current information. Questions about recent framework updates or new libraries got more accurate answers than Claude.

Where Gemini Struggles

1. Inconsistent Quality

Gemini is the least consistent of the three. One day it gives brilliant answers. The next day it struggles with basic questions.

I asked it the same React question twice, a week apart, and got vastly different quality answers. The first was excellent. The second missed obvious issues.

This inconsistency makes it hard to trust as a primary tool. I never know which Gemini I’m going to get.

2. Weaker Code Generation

For pure code generation, Gemini is third place. The code works, but it’s often not idiomatic or optimized.

I asked all three to write a Python function to parse complex JSON and transform it. Claude and ChatGPT gave me Pythonic code with proper error handling. Gemini gave me code that worked but felt like it was written by someone who learned Python last week.

3. Documentation and Community

There are fewer tutorials, fewer examples, and less community knowledge about working with Gemini effectively. When I hit a wall with ChatGPT or Claude, I can Google my way to answers. With Gemini, I’m often on my own.

4. Reliability Issues

Gemini has had more downtime and weird errors than the other two. I’ve encountered “model is busy” errors, long delays, and occasional responses that just cut off mid-sentence.

For a paid tool I’m relying on for work, this is frustrating.

The Real-World Speed Test

I ran the same coding task through all three and timed everything:

Task: Create a Laravel API endpoint that:

• Accepts a CSV file upload
• Validates it has specific columns
• Processes it in chunks to avoid memory issues
• Stores results in database
• Returns a progress indicator
• Has proper error handling

Claude:

• First response: 14 seconds
• Code quality: Excellent, included chunk processing, validation, and progress tracking
• Iterations needed: 2 (I asked for additional error cases)
• Total time to working code: 12 minutes
• The code just worked in production

ChatGPT:

• First response: 4 seconds
• Code quality: Good, but missed chunk processing initially
• Iterations needed: 4 (I had to ask for chunking, then progress tracking, then better error handling)
• Total time to working code: 18 minutes
• Needed minor fixes for edge cases

Gemini:

• First response: 7 seconds
• Code quality: Okay, worked but not idiomatic Laravel
• Iterations needed: 5 (multiple back-and-forth to get it right)
• Total time to working code: 23 minutes
• The progress tracking implementation was overly complex

Winner: Claude, despite being slower to respond. The comprehensive first answer saved more time than ChatGPT’s speed.

When I Use Each One

After eight months, here’s my actual workflow:

I use Claude (60% of my AI usage) for:

Complex debugging sessions When something is deeply broken and I need to think through it, Claude is my first choice. The longer context window and deeper reasoning help me understand what’s really happening.

Architecture and design decisions “Should I use a job queue or a cron job for this?” “How should I structure this feature?” Claude gives more thoughtful answers that consider trade-offs.

Understanding unfamiliar code When I inherit a codebase or need to understand how something complex works, Claude excels at explanation.

Code review I paste my code and ask Claude to review it. It catches issues I miss and suggests improvements that ChatGPT often doesn’t.

Refactoring Claude understands the bigger picture better, so its refactoring suggestions actually improve code structure, not just surface-level changes.

I use ChatGPT (30% of my AI usage) for:

Quick syntax questions “How do I do X in Y language?” ChatGPT’s speed wins here.

Boilerplate code CRUD operations, standard API endpoints, common patterns—ChatGPT generates these faster than Claude.

Current information When I need to know about recent updates, breaking changes, or new features, ChatGPT’s web access helps.

Rapid prototyping When I’m trying out ideas quickly and need fast iteration, ChatGPT keeps me moving.

Simple utility functions String manipulation, date formatting, array operations—ChatGPT is fast enough that I don’t break flow.

I use Gemini (10% of my AI usage) for:

Google Workspace integration When I need to work with data in Google Sheets or Docs, Gemini’s integration is convenient.

Visual debugging Sending screenshots of UI issues sometimes gets good results from Gemini’s vision capabilities.

Second opinion When Claude and ChatGPT disagree or I’m not satisfied with their answers, I’ll check Gemini.

Experimentation I still experiment with Gemini on new problems, hoping to find more use cases where it shines.

The Actual Cost Analysis

Let’s talk money. Are these worth $20/month?

My Monthly Usage (Average)

Claude Pro ($20/month):

• Used 18-22 days per month
• Average 45 questions per day on work days
• Hit rate limits 3-4 times per month
• Time saved per month: ~20-25 hours
• Cost per hour saved: ~$0.80-$1.00

Worth it? Absolutely. The time saved on complex debugging alone justifies the cost.

ChatGPT Plus ($20/month):

• Used 20-25 days per month
• Average 60 questions per day (lots of quick queries)
• Never hit limits
• Time saved per month: ~15-20 hours
• Cost per hour saved: ~$1.00-$1.33

Worth it? Yes, especially for the speed and reliability.

Gemini Advanced ($19.99/month):

• Used 8-12 days per month
• Average 15 questions per day
• Time saved per month: ~5-8 hours
• Cost per hour saved: ~$2.50-$4.00

Worth it? Debatable. The Google Workspace integration is nice, but I’m not sure I’d pay for it as a standalone coding tool. The 2TB storage makes it more justifiable.

Could You Get By With Free Tiers?

Claude: The free tier is very limited. You’ll hit limits quickly if you’re using it for real work. Not viable for daily development.

ChatGPT: GPT-3.5 on the free tier is surprisingly capable for simple questions. You could probably do 30-40% of your work with the free tier if you’re budget-conscious.

Gemini: The free tier is more generous than Claude but less capable than ChatGPT’s paid tier. Good for occasional use.

My recommendation: If you can only afford one, get ChatGPT Plus. It’s the most versatile. Add Claude Pro when you can—the combination covers 90% of needs.

The Frustrating Reality: They All Hallucinate

Let me be blunt: all three will confidently give you wrong answers sometimes.

Example 1: The Database Migration That Didn’t Exist

I asked ChatGPT how to roll back a specific type of Laravel migration. It gave me detailed instructions using a command-line flag that doesn’t exist. I spent 15 minutes trying to figure out why it wasn’t working before checking the actual docs.

Example 2: The API That Changed

Claude gave me perfect code for interacting with the Stripe API. Except Stripe had changed that endpoint three weeks ago, and Claude’s training data was older. The code failed with cryptic errors.

Example 3: The Framework Feature Mixup

Gemini confidently explained how to use a React 19 feature… that was actually a Vue 3 feature. The confusion wasted an hour of my time.

How to protect yourself:

1. Always verify critical information – If something seems too convenient, check the official docs
2. Test incrementally – Don’t paste 100 lines of AI code without understanding it
3. Watch for absolute certainty – When an AI says “definitely” or “always,” be skeptical
4. Check dates on answers – Ask “is this still current in 2025?”
5. Use multiple sources – If something is important, ask two different AIs and compare

The AIs are tools, not oracles. They make mistakes. The question is whether they save you time despite the mistakes—and for me, they absolutely do.

What About GitHub Copilot and Cursor?

People ask me this a lot. “Why use chat interfaces when you can have AI in your IDE?”

I use GitHub Copilot too (separate $10/month subscription). Here’s how they compare:

Copilot/Cursor are better for:

• Inline code completion while typing
• Autocompleting boilerplate
• Staying in your IDE without context switching
• Quick suggestions as you write

Claude/ChatGPT/Gemini are better for:

• Complex explanations and debugging
• Architectural discussions
• Understanding existing code
• Refactoring entire files or features
• Planning before coding

They’re complementary, not competitive. I use Copilot for autocomplete, Claude for thinking through problems.

My IDE setup:

• GitHub Copilot for suggestions as I type
• Claude in browser tab for complex questions
• ChatGPT in another tab for quick lookups
• VSCode terminal for running code

This combo lets me use the right tool for each type of task.

The Features That Actually Matter

After eight months of real usage, here are the features I care about:

Features I Use Daily:

1. Long context windows – Being able to paste multiple files and have the AI track everything is huge. Claude wins here.

2. Code artifacts/formatting – Getting code in a copyable format matters more than you’d think. Claude’s artifacts are nice but sometimes overkill. ChatGPT’s code blocks are simple and work.

3. Fast response time – Every second matters when you’re asking 40 questions a day. ChatGPT’s speed keeps me in flow.

4. Conversation history – Being able to return to previous conversations is essential. All three do this well.

5. Multi-language support – Switching between Python, PHP, JavaScript, SQL in the same conversation. All three handle this fine.

Features I Don’t Care About:

1. Voice input – Tried it once, never used it again. Typing is faster for code.

2. Mobile apps – I’m always at my computer when coding. Mobile access is nice but not essential.

3. Team features – Maybe useful for larger teams, but I work solo or in small groups where sharing links works fine.

4. Custom instructions – Theoretically useful, but I found myself just including context in each prompt rather than setting up elaborate custom instructions.

5. API access – For chat-based help, I want a UI. For programmatic access, I’d use different tools.

The Future: Where Are They Heading?

Based on their current trajectories and recent updates:

Claude is doubling down on being the “thoughtful” option. Longer context, better reasoning, more careful answers. If you need an AI to think, this is the direction they’re heading.

Anthropic recently released Claude 3.5 Sonnet, which is noticeably better at coding than previous versions. The trend suggests they’re focused on quality over speed.

ChatGPT is betting on speed, plugins, and ecosystem. GPT-4o is faster than GPT-4. They’re adding more integrations. OpenAI wants to be the platform everything else builds on.

The recent updates have focused on response speed and multi-modal capabilities. They’re clearly optimizing for being fast enough for real-time applications.

Gemini is leveraging Google’s integration advantages. Access to Google Search, Workspace, YouTube, etc. They’re betting that seamless integration with Google’s ecosystem will differentiate them.

Google’s recent Gemini updates have focused on multimodal capabilities and search integration. They’re playing to their strengths.

My prediction: In 12 months, we’ll have:

• Even longer context windows (1M+ tokens)
• Much faster response times across the board
• Better code execution and testing built-in
• More specialized models for different languages
• Tighter IDE integration

But the core question—”which one saves you the most time?”—will probably still depend on what kind of coding you’re doing.

The Honest Recommendation

If someone asked me today, “Which one should I subscribe to?” here’s what I’d say:

If you can only afford one subscription: Get ChatGPT Plus. It’s the most versatile, fastest, and most reliable. The web browsing and plugins add real value. You won’t hit rate limits on normal usage.

If you do complex backend work: Add Claude Pro. The deeper reasoning and longer context windows are worth it for debugging and architecture. The combination of ChatGPT for speed and Claude for depth covers most needs.

If you live in Google Workspace: Gemini Advanced makes sense as a third option, especially since you get 2TB storage. But I wouldn’t make it your primary coding assistant yet.

If you’re on a budget: Use ChatGPT free for 80% of tasks and pay for Claude Pro when you need deeper thinking. Skip Gemini unless you really need the Google integration.

My personal setup: I pay for both Claude Pro and ChatGPT Plus ($40/month total). I use my Google One subscription primarily for storage, and Gemini is a bonus. This combo handles everything I throw at it.

Real Talk: Are They Actually Worth It?

Here’s the uncomfortable truth: These AI assistants don’t write code for you. They help you write code faster.

You still need to:

• Understand what you’re building
• Architect the solution
• Verify the code works
• Test edge cases
• Debug issues
• Make final decisions

What they save you is:

• Syntax lookup time (“what’s the method name in Python for X?”)
• Boilerplate generation (“create a standard CRUD controller”)
• Documentation reading (“how does this library work?”)
• Debugging assistance (“why isn’t this working?”)
• Code explanation (“what does this regex do?”)

For junior developers: These tools are incredibly valuable for learning. But there’s a risk of using them as a crutch. You need to understand the code they generate, not just copy it.

For mid-level developers: This is where AI assistants shine most. You have enough experience to verify answers but can save huge amounts of time on routine tasks.

For senior developers: The value is in offloading mental overhead. Let the AI remember syntax while you focus on architecture and business logic.

My honest assessment: These tools have made me about 25-30% more productive. That’s not “10x engineer” productivity, but it’s substantial. I ship features faster, debug quicker, and spend less mental energy on syntax and documentation.

For $20-40/month, that’s a bargain.

The Things Nobody Tells You

After eight months, here are the non-obvious insights:

1. You’ll develop favorites based on your specific needs – I thought there would be a clear winner. Instead, I use different tools for different tasks. Your mileage will vary based on your tech stack and workflow.

2. Prompt engineering matters less than you think – I don’t use fancy prompt templates. I just describe what I need clearly. Being specific helps, but you don’t need to learn special techniques.

3. Copy-pasting code is usually fine – There’s no shame in it. We copy from Stack Overflow, we’ll copy from AI. What matters is understanding what you’re pasting.

4. The conversations are more valuable than individual answers – The real value is iterating on a solution through dialogue, not getting perfect code on the first try.

5. They’re getting better fast – I wrote the first draft of this article three months ago. I’ve already had to update it twice because all three AI improved significantly.

6. Context is king – The more specific context you provide (frameworks, versions, constraints), the better the answers. “I’m using Laravel 10 with PHP 8.2” gets better results than just “PHP.”

7. Treating them like a colleague helps – I talk to these AIs like I’d talk to a coworker. “Hey, I’m stuck on this. Here’s what I’ve tried. Any ideas?” works better than treating them like a search engine.

Common Pitfalls to Avoid

Pitfall 1: Trusting code without testing I once deployed AI-generated code to production without properly testing it. The code looked perfect. It had a subtle bug that only appeared under specific conditions. Always test.

Pitfall 2: Over-relying on AI for learning If you’re learning a new technology, don’t let AI do all the work. Struggle with it yourself first. Use AI for help, not as a replacement for learning.

Pitfall 3: Not checking for security issues AI-generated code doesn’t always follow security best practices. It might use deprecated functions, skip input validation, or have SQL injection vulnerabilities. Review security-critical code carefully.

Pitfall 4: Assuming consistency Just because an AI gave you a great answer yesterday doesn’t mean it will today. The underlying models get updated. Your prompts might be slightly different. Results vary.

Pitfall 5: Fighting with the AI If you’re on iteration #8 trying to get the AI to understand what you want, it’s time to either try a different AI or just write it yourself. Don’t get stuck in an endless loop.

The Bottom Line

After eight months and probably 5,000+ interactions across these three AI assistants, here’s my verdict:

Claude is the best for deep thinking, complex debugging, and understanding nuanced problems. It’s slower but more thorough. When quality matters more than speed, choose Claude.

ChatGPT is the best all-rounder. Fast, reliable, and capable of handling most common coding tasks. It’s the Swiss Army knife of AI coding assistants. When you need versatility and speed, choose ChatGPT.

Gemini is the most promising but least mature. It has unique strengths, especially with Google integration, but isn’t quite ready to be your primary coding assistant. When you need Google integration or visual capabilities, choose Gemini.

The real answer: Use more than one. ChatGPT for daily work, Claude for complex problems, and optionally Gemini for specific use cases. The combination is more powerful than any single tool.

These aren’t replacements for knowing how to code. They’re force multipliers. They take tasks that would take 30 minutes and reduce them to 10 minutes, Turn 4-hour debugging sessions into 2-hour sessions. They let you ship features faster and spend less time on Stack Overflow.

Are they perfect? No. Do they hallucinate? Yes. Will they occasionally waste your time with wrong answers? Absolutely.

But they also save me 20-30 hours per month. They help me solve problems I might not have solved alone. They make coding more enjoyable by handling the tedious parts.

For me, that’s worth $40/month. Your calculation might be different.

Now, if you’ll excuse me, I need to ask Claude why my database migration keeps failing, ask ChatGPT for a quick regex pattern, and possibly screenshot the error for Gemini to analyze.

Welcome to coding in 2026.