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Comment by Rapzid | Hacker News Reader

I use GitHub Copilot and unfortunately there has been a weird regression in the bundled Plan mode. It suddenly, when they added the new plan memory, started getting both VERY verbose in the plan output and also vague in the details. It's adding a lot of step that are like "design" and "figure out" and railroads you into implementation without asking follow-up questions.

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I find that even with opus 4.6, copilot feels like it’s handicapped. I’m not sure if it’s related to memory or what but if I give two tasks to opus4.6 one in CC and one in Copilot, CC is substantially better.

I’ve been really enjoying Codex CLI recently though. It seems to do just as well as Opus 4.6, but using the standard GPT 5.4

As a matter of interest are you using the copilot cli?

yeah. copilot cli using opus 4.6 vs claude code using opus 4.6

If you could share I’d be really interested in hearing a concrete example of the two behaving differently. I work in Microsoft (not on copilot - though I’m an heavy user, and use Claude code in a personal capacity) and would be quite happy to repro and report back to the copilot cli team who are responsive.

I have the same experience with Antigravity and Gemini CLI, both using Gemini 3 Pro. CLI works on the problem with more effort and time. Meanwhile, antigravity writes shitty python scripts for a few seconds and calls it a day. The agent harness matters a lot

Copilot feels like being a caveman, Claude code feels like modern times comparatively.

I think this shows that the model alone isn't the complete story and that these "harnesses" (as people seem to be calling them) shape a lot of the experienced behavior of these tools.

My analogy is that the model is the engine and the harness is the driver and chassis.

You can have the biggest monster of an engine ever, but if you put it in a tricycle and a grandma is driving, you won't get good results.

Opus 4.6 has a 200k context limit in Copilot. Could be the issue.

IIRC the context limit in Copilot is actually 128k, not 200k.

200k is the normal context limit elsewhere.

> VERY verbose in the plan output

Is that an issue? GitHub charges per-request, not per-token, so a verbose output and short output will be the same cost

What model are you using?

The problem might be that our brains charge per token, which makes reviewing hard. :)

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