Why I Ran This Test I use all three models daily for coding. But I’ve never put them head-to-head on the exact same tasks. So I designed 5 real-world coding challenges and ran each model through them. No synthetic benchmarks. No cherry-picked examples. Just everyday dev work. The 5 Tasks Refactor a 400-line Express router into a layered architecture Debug an async race condition Generate CRUD endpoints from an OpenAPI spec Document a 2000-line legacy codebase Write unit tests with edge case coverage Each task was run 3 times per model; I picked the best output. Deep Dive Refactoring – Claude Wins Claude didn’t just split the code – it understood the architecture. It identified two circular dependencies I hadn’t even noticed and proposed clean solutions. GPT’s output was solid but missed a middleware injection edge case. Gemini got the job done but with inconsistent naming conventions. Debugging – Claude Edges Ahead All three found the race condition root cause. The difference was in the fix quality. Claude’s solution included mutex locking, retry logic, and timeout handling. GPT pointed in the right direction but left boundary handling as an exercise. Gemini suggested a mutex but forgot about timeout scenarios. Code Generation – GPT is King Given an OpenAPI spec, GPT-5.4 produced complete CRUD routes, validation middleware, and error handlers in record time. The code was nearly copy-paste ready. Claude was slightly slower but marginally higher quality. Gemini was middle-of-the-road here. Long Context – Gemini Shines This is where Gemini’s massive…
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