Why does Cursor produce richer/more accurate outputs than Claude Code with the same Opus 4.6 Extended Thinking model?

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I’ve been testing both Cursor IDE and Claude Code CLI with the exact same model (Claude Opus 4.6 with extended thinking enabled), and I’m consistently noticing that Cursor produces noticeably richer, more detailed, and more accurate outputs for the same prompts.

Both are supposedly using the same underlying model with extended thinking, yet the quality difference is significant. Cursor seems to:

  • Generate more comprehensive solutions

  • Provide better context awareness

  • Produce fewer iterations to reach the correct answer

  • Give more detailed explanations

My Questions:

  1. Is there something fundamentally different in how Cursor wraps/implements Claude’s API compared to Claude Code’s native implementation? Could there be additional prompt engineering or system prompts that Cursor adds?

  2. Could the IDE integration itself be enhancing the model’s performance? Does having full IDE context (open files, project structure, etc.) actually result in better API calls being made?

  3. Are the extended thinking parameters configured differently between the two tools? Is Cursor perhaps using higher token budgets for thinking, or different effort levels?

  4. Is anyone else experiencing this? Or am I just experiencing confirmation bias?

I’m trying to understand if this is a technical difference in implementation, or if I need to adjust my Claude Code configuration to match what Cursor is doing.

Any insights would be appreciated!

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Yes, Cursor has their own set of ‘rules’, system prompt and I am sure many other things, that specify how the model should behave in the IDE. I also think Cursor has a small model helping during the conversation, although it might have been replaced by subagents (e.g. Explore).
Cursor takes time to properly integrate models into the IDE, some competitors just add the model without testing its outputs and performance. When cursor finds an issue with the model (e.g. formatting problems in outputs - e.g. each word is on a new line - they fix it on their side - so in other tools the model might still have this problem).