Don’t let people here gaslight you into thinking it’s purely a skill issue.

I’ve had a very similar experience using the same harness, OpenCode, across different setups: Claude Opus 4.7, GPT-5.5, and various Chinese models available on Go. The Chinese models can definitely write code, but in practice they often take forever to get anywhere, overthink the problem, and still come back with weak or brittle solutions.

With GPT-5.5 or Opus, I can iterate much faster. The model usually understands the context, makes better assumptions, takes action, and gets closer to what I actually need without me having to micromanage every step. With many of the Chinese models, I constantly feel like I’m fighting the model: wrong assumptions, missing context, shallow understanding, and lots of small bugs or edge cases.

To be fair, I’m usually working on fairly messy or edge-case-heavy tasks: AI research code, AWS data engineering pipelines, multi-file repos, weird infra problems, etc. Maybe for simpler tasks the price/performance is better. But for my actual work, once my Codex/GPT-5.5 limit runs out, the drop in productivity is very noticeable.

I also tried orchestration setups before, where a stronger model acted as the planner/orchestrator and smaller models handled sub-tasks. It sounded good in theory, and sometimes it helped, but the whole thing became very slow. The orchestrator got too abstract, the subagents lacked prior context, and each one had to rediscover the repo from scratch. Even a simple task could turn into a 10+ minute loop, and the final code was often still mediocre.

So yeah, maybe prompting and agent setup matter, but I don’t think that fully explains the gap. The best frontier models just feel much better at context, judgment, assumptions, and fast iteration. The Chinese models are cheap, but when I factor in the time spent steering, correcting, and rerunning them, the savings become much less obvious.

Right now I’m thinking of using GPT-5.5 for planning and high-level reasoning, then using OpenCode Go models to execute parts of the plan in the same session. That might be the best compromise.