Spoiler alert. Claude is 10 million times better! Okay, maybe not that much but it’s enough to make me overlook its shortcomings. I just finished off a new vibe coding test, and in the process, I gave both Claude and GitHub Copilot a head-to-head. This is an unfiltered breakdown of my experience; both have their distinct advantages and disadvantages, but from a capability perspective the results were night and day.

Show me the money

Claude Code at face value seems to be the most expensive, at least on the variable spend model compared to CoPilot's $34 per user per month. Both Claude and Copilot have fixed-rate licenses, which is a great advantage for a company looking to stick within a budget. But I wonder how far you would reasonably get with Claude's biggest $100 package when I can easily do $44 a day on their variable spend. Claude's variable spend offers immense power, but it can also be a serious liability if left unchecked. While I do feel it made me much faster, racking a $44 bill in a day is quite high and presents issues if you have ambitions of scaling AI use across an organization. I reckon you can conservatively manage with around $120 per month on Claude, but to truly maximise its value, I suspect you’d need to be spending $300 or more per developer to see strong returns. Copilot is by no means infinite; I ran out of regular credits on the 25th of the month, which is what provoked the move to Claude. The reality is that this is still quite expensive, but all signs points to the cost of inference decreasing dramatically in the months and years to come.

Brains vs Brawn

Let’s be clear, GitHub Copilot with GPT models does work, but it’s really not great. The fixed subscription, while predictable, only gives you a set number of calls for the month, and if you use a premium model like Claude Sonnet, you burn through your premium tokens. Once your premium tokens are gone—and for a power user, that can happen in as little as 5 days—you're stuck with GPT 4.1. GPT 4.1 just struggled with everything; it wouldn't listen to my development preference no matter how articulate I was. I wanted code that was still supportable by a human. GPT still created stuff wherever it felt, and created structural issues everywhere! I routinely found myself editing its code, often making it even more messy by having to add yet more inline CSS. It couldn’t understand MVC no matter how much I spelled out the structure. The server-side code was even more atrocious; it would do the silliest things like select everything from the database, loop through it, and do a lowercase text if statement instead of just doing a lookup from the database. Claude by comparison seems to have a far better time dealing with code tasks. It was able to instantly understand my code structure just by dropping it into the folder. In many cases, it fixed the structural issues GPT had introduced and did a far better job adhering to the style out of the box. Brownfield environments are one of my biggest personal problems, and this test at least showed that Claude can drop into an existing, messy codebase and still add value. Debugging was much easier because I could just ask it to describe what was influencing an outcome and then fix the function that GPT had created in some obscure manner. I am being somewhat overly critical of GPT-4; I was able to complete the project with it after all, which speaks volumes to its capability at that price point, making it a serious consideration for those wanting to get involved with AI and still have a predictable and useful spend band. But it was significantly frustrating; at some points it almost didn't feel worth the benefit of having an AI agent.

User Experience

Copilot’s biggest benefit is that it just feels like an organic way of writing code. You can collaborate better with the AI, especially when using its premium model like Claude Sonnet, because you can easily drag context like images and documentation into the chat window. This is a big benefit of having a GUI. By contrast, Claude's command-line interface gives me PTSD of my early development days when I had to remember how to get out of vi—"is it Ctrl+I or D or is that the nano editor"? The command-line interface really is a buzz kill and is quite clunky; writing a medium size prompts and then having to backspace forever. And it’s surprisingly more of a “Jesus Take The Wheel” type of development than Copilot, where I can at least see the code in front of me. That being said, you can still collaborate with Claude's command line by just having your editor open on the side. But honestly, I found that I didn't have to look at the code nearly as much as I did with ChatGPT. Even debugging was significantly easier; it did a really good job understanding the code and explaining what it was doing, so that I could give better prompts on how I wanted to see data, structure, and formulate code. So the command-line interface is a major drawback, but I’m sure the boys and girls at Anthropic are frantically vibe coding their way through a Claude IDE that they will give us soon.

Tips & Tricks

Here are a few interesting lessons I learned in using AI: The biggest advantage of these AIs is being able to use rich context or multimodal input. I regularly do quick wireframes of what I want to see, or bring in images of sites I like for styling guides or external documentation. This is what makes Copilot such a breeze to use; you can just drag and drop stuff into it. This is a less intuitive with Claude's command line, but you can do it other ways. I went with storing wireframes and other requirements planning in a folder. This is probably a better way because it was how I ended up collaborating with the AI. When it came to debugging front-end issues, Playwright was my best bet; I'd have it call pages on my site and then evaluate what it sees to fix styling and even errors, and it did an excellent job here. Then finally, use Git to set markers when your AI gets it right. This thing writes huge swathes of code at a time across multiple files. You can't use ctrl-z like you would with regular code. So, if it gets it wrong, you can just do a git reset --hard, refine your prompt, and try again. Your results will get better faster than trying to grind through inevitable mistakes.

Final Verdict

If you can afford it, go with Claude. The terminal use is worth the sacrifice. GitHub Copilot gives you predictable spend, but can be quite frustrating to use with limited access to premium models. With a tool like Claude, I can actually focus on the stuff around the code, like setting a "Code Preference or Architectural Guide Rails." I recall Jensen Huang talking about how natural language should just be how we start writing code. This feels pretty damn close. That being said, I'm still not sure this strictly speaking gives the average Joe the ability to write large-scale systems. I could be wrong, but I feel like I end up describing some parts of my code, a lot of my architecture, and go through the same requirements refinement process as I do when writing systems the traditional way. In that sense, this does feel like just the next level of abstraction, like when we went from assembly to high-level languages.