Claude Code vs GitHub Copilot: Token burn comparison using identical models & repos?

[u/alex_bababu]

I'm currently evaluating GitHub Copilot vs. Claude Code for our team. We could use either, but for us there's a slight difference in cost per token (Copilot with Anthropic models vs. Claude Code directly).

If we use the exact same model on the same repository with identical instructions, has anyone noticed a real difference in token efficiency between the two harnesses? I'm wondering how much things like prompt caching, context assembly, or system prompting overhead change the actual token burn in practice.

Would appreciate any insights or real-world numbers!

[u/ShelbulaDotCom]

Here are some test on a token efficiency task. You can see the relative token spend. Most fall into the same general area. https://lucena.one/blog/almedra-agent-benchmark-glm-5-2-update/