Claude vs GPT-4 vs Gemini for Autonomous Agent Tasks: My Production Benchmark

I spent three weeks and about $340 benchmarking three LLMs on the actual tasks my autonomous agents run in production. Not the demo tasks. Not "summarize this article." The unglamorous, repetitive, occasionally weird tasks that keep a six-agent system running. Here's what I found, including the parts that surprised me. Why this benchmark is different Most LLM benchmarks test general reasoning on clean, standardized tasks. That's useful for comparing models in theory. It's less useful for answering "which model should I pay for when my agent needs to do X twelve times a day, every day, indefinitely." My agents perform four categories of tasks: Content generation — drafting posts, writing summaries, creating structured data from unstructured inputs Code review and generation — reviewing PRs, generating utility functions, catching obvious bugs Planning and task decomposition — breaking a goal into subtasks, prioritizing a backlog, deciding what to work on next Tool use and structured outp