Context Windows Are Mostly Marketing Bullshit
Claude brags about 1M tokens, Gemini says the same, but anything over 30k tokens and these models start making shit up. I fed our entire React codebase (about 800k tokens) to Claude and it confidently told me about components that don't exist and import paths to nowhere.
GPT-5 at least has the decency to throw timeout errors instead of lying to your face. After testing all three with increasingly large codebases, the sweet spot is 20-30k tokens max. Beyond that, you're gambling with hallucinations.
Long debugging sessions are where this really bites you. The conversation history gets longer, the models get confused, and suddenly they're referencing solutions from 3 hours ago that don't apply to your current problem. I've had to restart conversations more times than I care to admit.
Benchmarks vs Reality Check
Claude's famous 72.7% on SWE-bench means jack shit when it won't help you audit security vulnerabilities because "this could be harmful." Spent half a day trying to get it to analyze authentication code without triggering the safety nanny.
GPT-5's reasoning mode is genuinely better at complex problems, but it's slow as molasses. What used to take 3 seconds now takes 45 seconds to 2 minutes. Great for deep analysis, terrible when you're debugging a prod outage at 2am.
Gemini 2.0 screams through simple tasks but chokes on anything complex. Tried to use it for image analysis and got RESOURCE_EXHAUSTED errors on files over 1.5MB. Their "multimodal" capabilities work great in demos, not so much in production.
The Real Cost Breakdown (Prepare Your Wallet)
The advertised pricing is complete horseshit. Here's what actually happens:
Claude costs more per token but wastes less of your money on retries. Still charges you for requests it refuses to answer, which is infuriating when you're debugging auth code.
GPT-5 looks reasonable until reasoning mode kicks in and burns through tokens like a crypto miner. Rate limiting hits during business hours when you actually need it to work.
Gemini is cheap until you factor in the constant retries. Tasks that should work the first time need 2-3 attempts, which kills the cost advantage.
Our team budget went from $2k/month to $6k/month by month three. Nobody warns you about the retry costs, failed requests, and the hours you'll spend troubleshooting integration issues.
How Teams Actually Use These Things
Most dev teams end up using Claude for code reviews because it's good at finding bugs, despite the safety theater. GPT-5 gets used for research and documentation when you can wait for the slow responses. Gemini handles quick queries and content generation when it's working.
Product teams discovered GPT-5's reasoning mode is actually useful for market analysis, but they keep Claude around for technical specs because it writes better documentation.
The smart teams use AWS Bedrock or Azure OpenAI to switch between models automatically. When one service is down or rate-limited, the system fails over to another. This costs more but saves your sanity when everything breaks at once.
Multi-model routing sounds fancy but it's really just "use the one that's not broken today" logic with some cost optimization thrown in.
