[nsingh2]

This stuff is a little messy and opaque, but the performance of the same model in different harnesses depends a lot on how context is managed. The last time I tried Copilot, it performed markedly worse for similar tasks compared to Claude Code. I suspect that Copilot was being very aggressive in compressing context to save on token cost, but I'm not 100% certain about this.

Also note that with Claude models, Copilot might allocate a different number of thinking tokens compared to Claude Code.

Things may have changed now compared to when I tried it out, these tools are in constant flux. In general I've found that harnesses created by the model providers (OpenAI/Codex CLI, Anthropic/Claude Code, Google/Gemini CLI) tend to be better than generalist harnesses (cheaper too, since you're not paying a middleman).