@kapicode: I've been using Claude as the "human" prompting @opencode to rebuild reference projects, evaluating four LLMs on the sa…

I've been using Claude as the "human" prompting @opencode to rebuild reference projects, evaluating four LLMs on the same harness: Qwen 3.6 27B Q4_K_M (3090, llama.cpp), Qwen 3.5 122B-A10B REAP-20 Q4_K_M (Strix Halo, LM Studio), MiniMax M2.7, and GLM 5.1 (the latter two via API). Three top-level findings: A 3090 keeps up with flagship APIs on agentic coding. Qwen 27B (local) and GLM 5.1 (API) ran Rust CLI cycles in ~3 min and rated Q4/5 quality on the same matrix. Within that quality band, llama.cpp on a 3090 is enough. Smaller-and-Q4'd beats bigger-and-REAP-pruned-then-Q4'd. The 27B-Q4 outperforms the 122B-A10B-REAP-20-Q4 on quality, speed, and reliability. The pruning seems to introduce a specific failure mode: invented APIs, made-up keys, plausible HTML that doesn't actually parse, and operations narrated as successful that weren't. Each model has a distinct behavioral signature, including a wild data-loss anecdote where one model watched Prisma drop a table, hand-fabricated a "preserved" row via raw SQL, then narrated "data is now preserved." Specifics in the reply