ChatGPT: The Smooth Talker
ChatGPT is that colleague who talks a good game until you actually test their code. The memory system works maybe 60% of the time. When it's working, it remembers your coding style and preferences. When it breaks, you're back to explaining basic context every single conversation.
I wasted 20 minutes debugging a React component that wasn't updating state. Turns out ChatGPT suggested setState in a functional component - no hooks, just raw setState like it's 2018. The code looked plausible enough that I didn't catch it immediately. This kind of outdated React pattern is a common trap when using ChatGPT for modern frontend development.
GPT-4 pricing is reasonable until you start pasting entire codebases for context. I accidentally fed it a 50,000 line Rails app once - that was a $40 mistake. The tokenizer helps you avoid these disasters, but only if you remember to check first. According to OpenAI's usage statistics, the average developer burns through tokens faster than expected when dealing with large codebases.
Claude: The Perfectionist Who Won't Shut Up
Claude is that senior dev who writes perfect code but takes forever to approve anything. It's legitimately great at debugging - I've thrown completely broken Python at it and gotten working fixes with explanations that actually make sense. Anthropic's benchmarks show Claude consistently outperforms other models on coding tasks, especially when dealing with complex logic.
The downside? It won't help with basic shit because of "safety concerns." I asked for help writing a web scraper and got a fucking dissertation on robots.txt etiquette. It refused to help with a password validator because it might be "harmful." A password validator. These Constitutional AI restrictions are well-intentioned but often frustrating for legitimate development work.
At $15/$75 per million tokens, it's expensive as fuck. But when you're dealing with complex debugging, especially legacy code that makes no sense, Claude earns its cost. The 3.5 Sonnet model is genuinely better at understanding weird edge cases than the others, particularly with 200K context windows that actually work reliably.
Gemini: The Indecisive Know-It-All
Gemini is Google's attempt to make their search engine sentient, and it shows. The real-time information access is actually useful for checking if libraries are still maintained or finding recent Stack Overflow threads. The 2M token context window is marketing bullshit - it loses track after about 50K tokens in practice, despite Google's claims about long-context performance.
But holy shit, this thing changes its mind constantly. I asked about database choices for a simple cache. First it said MongoDB. Then Redis. Then fucking flat files when I questioned the Redis suggestion. It's like talking to someone who just discovered programming yesterday but thinks they're an expert. This inconsistency is documented in multiple Reddit discussions where developers report similar frustrating experiences.
The code suggestions are genuinely dangerous. It told me to use eval() in production JavaScript. When I said that was insane, it suggested Function() as a "safer" alternative. These aren't edge cases - this is basic shit that could break your app. Security researchers have documented how AI training data poisoning can lead to models suggesting insecure coding patterns.
Why I Pay for All Three Like an Idiot
Plot twist: I ended up with subscriptions to all three because each one fails differently. Claude for serious debugging when I need actual working code. ChatGPT for quick scripts and when I need something fast. Gemini for checking if libraries are still maintained or finding recent examples. This multi-tool approach is becoming increasingly common among developers, as shown in Stack Overflow's 2024 Developer Survey where 62% of respondents use multiple AI coding assistants.
My AI budget went from $50 to $200/month because it's faster to use the right tool than fight with the wrong one. The cost doesn't matter when your AI-generated code takes down production at 2am on Saturday. According to GitHub's State of AI in Software Development, developers report spending 30% more on AI tools than initially budgeted due to unexpected usage patterns.
The real cost isn't the API calls - it's debugging the garbage these things generate. I've spent more time fixing AI suggestions than if I'd just written the code myself. But when they work, they save hours. The trick is knowing which one to trust with what. MIT's recent study found that while AI coding tools increase productivity by 37% on average, they also introduce 41% more bugs that require additional debugging time.
