I replayed the same 27 real production tasks through a local LLM and the frozen Claude baseline to see if a local model could replace Claude as the brain behind a 90‑tool personal agent. The test used a single RTX 3090 with a 16K context, and the local 30B model scored 22.8/100 versus Claude’s 89.4. The agent, built on Lang Graph, connects to about 90 tools like email, calendar, notes, files, Office, WhatsApp, Discord, and image generation.

After upgrading to three RTX 3090 GPUs with a 256K context, a model achieved 80.0, eliminated malformed tool calls (0 of 27), and cost about $0.000969 per task, roughly 787× cheaper than Claude’s $0.763. The cost metric uses measured GPU energy logged in Home Lab Monitor, converting it at my dual‑rate tariff with no per‑task billing.

Key caveats: model size and context changed together, so the comparison reflects a system upgrade, not an isolated model effect. A bias exists because the judge is a Claude model, possibly favoring its own outputs. Despite this, the numbers show a dramatic performance and cost gap, and the earlier 17‑task benchmark win does not guarantee generalization to this larger, tool‑rich setup.