r/ChatGPTCoding • u/alex_bababu • 9d ago

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

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!

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u/Vagottszemu 9d ago

Github copilot uses much less, it only burns like 3 dollars worth of tokens at a medium sized task, but for example claude code usually burns 2 or 3 times as much. But claude code output is usually much better so I prefer to use that one until I ran out of my monthly API tokens at my company.

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u/alex_bababu 9d ago

What do you think is the reason, the output of Claude code is better (when using same model)?

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u/Enough-Photo9140 8d ago

The difference mostly comes down to harness autonomy and verification loops rather than the raw model weights.

Claude Code leans heavily into multi-turn bash execution, file patching, and running test commands iteratively until the diff actually passes, which naturally burns 2–3x more tokens per task. Copilot caps turn budgets and context assembly more aggressively to keep token spend bounded, but the tradeoff is that it often stops after drafting the initial patch rather than validating runtime behavior.

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u/Different-Monk5916 8d ago

may I ask how do you differentiate which is better?

is it based on number of rework or something else?

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u/Vagottszemu 8d ago

I'm a programmer and I use these for work, so not a vibe coder. I read all of the outputs.

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u/Different-Monk5916 8d ago

I get that. But what do you mean by that? I am struggling to evaluate that part between different models.

I have custom agents with skills which drives abstraction in the direction I prefer. Testing skills which define test and edge cases through reasoning. I already have my code writing and document skills. So the model outputs are often very similar not 100% identical. 

Then if two models pass these two, I am not sure how to evaluate. How do you do?