100% to 0%: What Happened When I Stress-Tested a Local 35B Model's Tool-Calling

I've spent the last few weeks building a benchmarking desktop app for local LLMs — think of it as a dashboard that sits on top of llama.cpp / Ollama and answers the question every local-model tinkerer eventually asks: "is this model actually good, or does it just look good in a chat window?" Here's the short version of what I found testing a 35B quantized model: it aced a five-task "easy" agentic suite with a 100% pass rate , then I bumped the difficulty up one notch and it went to 0/54 . Zero.…