Oh My Opencode Review: Honest Results, Billing Risks, and When It's Worth It

Oh My Opencode promises a "virtual AI dev team" — Sisyphus orchestrating specialists, tasks running in parallel, and the magic ultrawork keyword activating all of it. That promise holds up for the right workload. For the wrong one, it adds cost and complexity without improving results. This article covers what actually happened in hands-on testing, and what the broader community has found after months of real usage. If you are new to the stack, start with the OpenCode quickstart to get the base agent running, then the Oh My Opencode quickstart and then the specialised agents deep dive This article assumes you are already familiar with the system and want to know whether it actually performs. For the wider picture of how OMO fits into the AI coding toolchain, see the AI developer tools overview. Oh My Opencode Performance: Hands-on Testing Results I ran the same tests I use for bare OpenCode — the same LLM benchmark tasks I ran against OpenCode with local Ollama and llama.cpp models. Th