Which AI Actually Helps You Code (And Which Ones Waste Your Time)

Claude is the one that doesn't give up when your shit breaks. I've had Claude spend 4 hours with me chasing down a memory leak in our Express middleware that turned out to be - I shit you not - a single missing await keyword. GPT suggested three different solutions that all missed the async issue entirely. Yeah, GPT might score higher on some benchmarks, but Claude just keeps digging until your tests pass.

Why Claude Actually Fixes Things:

Claude's debugging approach: Iterative problem-solving with persistent context retention

Claude keeps trying different approaches when the first fix doesn't work. It's like having a stubborn colleague who won't give up. Other models suggest something, it breaks, and they just shrug. Claude will go "okay, that didn't work, let's try this other approach" and keep iterating until your tests pass - at least in my experience with Django and React projects.

I've thrown huge codebases at Claude - like 10-15K lines maybe - and it seems to remember what we talked about way back in our conversation. When you're debugging some shit that spans multiple files (and let's be honest, all the interesting bugs do), Claude can usually hold that entire context in its head while you're both figuring out why your API is returning 500s.

The Safety Theater Problem:

Here's where Claude gets annoying as hell. Its safety features are way too aggressive. It won't help you build a web scraper because it thinks you're going to DDoS someone. It refuses to validate passwords because "security concerns." Even for totally legitimate use cases, you'll spend time convincing it you're not a bad guy.

Pro tip: If Claude won't generate something, just explain the business context. "I need to validate user passwords for our authentication system" usually works better than "help me validate passwords."

The Speed Tax:

Claude is slow as molasses - takes forever, like 3-4 seconds usually. When you're in the flow and just want quick answers, this is painful. But here's the thing: Claude gets shit right more often, so you waste less time fixing its mistakes. I'd rather wait a few seconds for working code than get instant garbage that I have to debug for an hour. The rate limits also kick in faster than other APIs.

Worth the Premium Pricing:

Claude used to cost way more - like 50% higher than GPT until early 2025. But Anthropic adjusted pricing this year, and now it's actually cheaper: Claude at $3/$15 vs GPT at $5/$15 for input/output tokens. Still worth it though. When you're debugging production issues during a late-night outage, that extra cost over Gemini pays for itself. I've had Claude save me from rollbacks that would have cost way more than a few API calls.

The math: If Claude saves you even one hour of debugging per month, it's already paid for itself in developer time. Check the cost calculator to see what your actual usage will be.

GPT-4 Turbo is the Toyota Camry of AI coding assistants - reliable, gets you where you need to go without any surprises. At 1.8 seconds per response and $5/$15 per million tokens, it's fast and reasonably priced. Good for quick prototypes when you need speed. Check the rate limits guide before you hit production though.

Why GPT Works for Most Shit:

GPT knows a little bit about everything. Need a React hook? It's got you. Django ORM query? Yep. Vue composition API? Sure thing. FastAPI endpoint? Done. It's like that senior dev who's worked at 15 different companies and has war stories about every framework.

For hackathons and MVP development, GPT works pretty well. It's fast enough to keep up with your caffeine-fueled coding sessions, and the mistakes it makes are usually obvious enough to catch quickly. I've built entire prototypes in a weekend just bouncing ideas off GPT - though after the last OpenAI update broke my workflow, I've been more frustrated with it.

The Speed vs Accuracy Trade-off:

Here's the thing about GPT giving up easily: I had a `Warning: Maximum update depth exceeded` error in React 18.2.0 with our auth component. GPT's first suggestion? Downgrade to React 17. When I said that wasn't an option, it suggested wrapping the entire component in useEffect(() => {}, []) - which is fucking insane and would break everything. Classic GPT - throws shit at the wall instead of thinking about why the state update loop is happening.

But for simple bugs and straightforward implementations, this usually doesn't matter. GPT's mistakes are usually shallow - wrong function name, missing import, basic syntax error. Easy to spot and fix, at least for the projects I've worked on.

Memory is a Joke:

ChatGPT's memory feature is absolute garbage. It'll remember your project setup for exactly one session, then forget everything and ask you to explain your architecture again. Context window problems are a real pain for long-term projects.

Gemini: The Brilliant Intern Who Can't Be Trusted

Gemini 2.5 Pro is like that intern who aced all the coding interviews but somehow can't ship working code. Those 98.9% HumanEval scores look impressive until you actually try to use it for real work. Check out the performance issues users are reporting to see what I mean.

When Gemini Doesn't Suck:

Gemini writes beautiful code when the stars align. For algorithmic problems and clean data processing tasks, it'll sometimes give you something that looks like it belongs in a computer science textbook. The 85.1% MBPP+ performance seems legit - when you give it a well-defined problem, it often produces cleaner solutions than the others.

It also seems to know the latest JavaScript frameworks better than the others. If you're working with React 18 concurrent features or the newest Vue composition patterns, Gemini might know about them before the documentation is even finished. Sometimes suggests deprecated APIs from 2019 like they're cutting-edge though. Do they even test this thing?

The Context Window Lie:

Context Window Reality: Claude 200K (works), GPT 128K (sufficient), Gemini "2M" (fails after 50K)

Google claims 2 million tokens of context, but that's mostly marketing bullshit. The context window was supposed to be the game-changer, but it's more like false advertising. After about 50K tokens, Gemini starts acting like it has dementia. It'll suggest functions that contradict what you discussed 20 minutes earlier, reference variables that don't exist, and generally lose track of what you're building.

I've had Gemini suggest refactoring our working JWT middleware into a singleton pattern - which immediately threw TypeError: Cannot read properties of undefined (reading 'tenant_id') because it killed our per-request context. Twenty minutes later, it suggested the exact opposite approach like it had never seen the code before. It's like pair programming with someone who has short-term memory loss.

The Reliability Problem:

This is where Gemini gets dangerous. It'll confidently suggest eval() for parsing user input, recommend deprecated APIs, and occasionally write code with SQL injection vulnerabilities. Security researchers keep finding examples of Gemini suggesting genuinely dangerous patterns. The red team testing guide shows some scary examples.

The worst part is how it changes its mind mid-conversation. You'll be implementing something, and halfway through, Gemini will suggest a completely different approach and act like the first suggestion was obviously wrong. Every time they update the model, my prompts break. Classic Google - fix one thing, break three others.

Google Integration: The Only Saving Grace

If you're already deep in the Google ecosystem, Gemini Code Assist does integrate nicely with Cloud Platform and Firebase. It's the only reason I haven't completely written off Gemini.

Plus, the real-time search integration can be useful for finding current library versions, though it sometimes pulls in outdated Stack Overflow answers and treats them as gospel. Tried using Gemini for our notification system last month - it suggested `node:crypto` imports that break in Node 18.2.0 because of an experimental modules bug. Ended up rewriting everything when it also suggested storing JWT tokens in localStorage like it's 2015.

So which one should you actually use? Here are the questions every developer asks...