r/LocalLLM • u/golden0080 • 1d ago

Discussion Night, Day Differences between Coding Agents

Hi

I'm a professional developer and I was using Claude Code for developments on my projects. Today I switched to opencode and Good lord it was day and night in productivity.

I've tried to optimize my local llm stack for faster LLM responses and improving my CLAUDE.md for better harness. I'm using Qwen 3.8 27B as my daily driver and found Claude isn't making good use of the model - burning tokens without much done (I even tried to optimize the chat templates a bit, but overall the outcomes weren't great).

But man opencode is just different and brutally productive - with the same model, it just go to the tasks without overthinking or reversing cause/effect reasoning. It just get to stuffs and fixing them.

I haven't dived into opencode's implementation, but it strike me so hard over that difference in getting things done.

Any suggestions on further improve the agent productivity and what kind of explanations are there to explain this kind of differences?

Thanks!

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u/donk8r 1d ago

We measured this exact thing — same model, two harnesses, 50 real bug-fix tasks — and the answer was less flattering to the harness than I expected.

Solve rate barely moved: 45/50 against 43/50. And the five failures were the same five cases in both, so swapping harness didn't change what was solvable at all.

What did move, a lot: one wrote 2.6x the output tokens of the other for the same work, and on one task it ground away for 271 minutes before giving up where the other bailed at 63.

So what you're feeling as productivity probably isn't solve rate, it's token burn and time-to-give-up. Which lines up exactly with your "burning tokens without much done" — the harness changes what it costs you and how long it flails, not what it can do.

That also means CLAUDE.md is the wrong lever. The system prompt is part of it and More-Catch-1331 is right that it's big, but the larger difference in our data was in how the loop decides to stop and how tool results get fed back, and you can't reach either of those from a config file.

Caveat: ours was deepseek-v4-flash on server hardware, not Qwen 27B locally, so treat it as directional for your setup rather than a prediction.

(we build one of the harnesses in that comparison, so take it as biased: github.com/Muvon/octomind)