I published a post last week about replacing gpt-oss:120b with Qwen 3.6 on my MacBook Pro M3 Max. The numbers in that post were real, but one set of tests was structurally gameable — 38 of 40 baseline images were the same class, so an “always-say-A” stub also scored 95 percent. I went back, designed three un-gameable reasoning tasks, and ran them against both local models on identical hardware. gpt-oss:120b finished the three tasks in 137 seconds. qwen3.6:27b-q8_0 took 1593 seconds —…

I spent two days benchmarking three Qwen3.6 variants against gpt-oss:120b on my MacBook Pro M3 Max. The shocking result: a 21 GB coding-tuned model ran an OpenClaw-shaped research-brief workload that I use for the non profit MACONA.org in 6 seconds — 10x faster than gpt-oss:120b on the same prompt. Fast enough that I now have reasonable confidence I could move this kind of work off the SaaS-hosted frontier models I have been paying for and onto local hardware on my dev machine. The deeper…

Five OpenClaw agents run the content and executive-assistant pipeline for MACONA — a nonprofit I volunteer with — at $0.72 a day. The most common support question on r/OpenClaw is “$25 in 9 hours, help.” The gap between those two numbers is three config decisions stacked on top of each other: heartbeat interval, model on the loop, and hours of operation. None of them are clever. All of them are usually set wrong by default.