I noticed something quite interesting lately — using Claude Sonnet 4.5 inside Claude Code CLI feels different from using the exact same model inside Cursor, Windsurf, or other IDEs.
And honestly, Claude Code CLI outperforms the rest by a large margin.

Even though both environments call the same LLM — Claude Sonnet 4.5 — the experience isn’t the same at all. The CLI feels deep, consistent, and capable of long reasoning. The IDE integrations is just not as good even I plug in the same model Sonnet 4.5.

I haven’t yet compared GPT-5-Codex directly between its CLI and IDE integrations, but from what I’ve heard, people say Codex CLI even beats Claude Code CLI, especially in long-horizon reasoning and strategic task planning. However, when I plug GPT-5-Codex into IDEs, I don’t feel that same superiority at all.

So what does that suggest?
The wrapper matters.

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The wrapper, or what some people call the “agentic layer,” makes a huge difference. A wrapper directly developed by Anthropic for their own model is not the same as one written by a third-party IDE team that happens to support multiple models. Tools like Cursor or Windsurf are built to be flexible — they can connect to OpenAI, Anthropic, Gemini, Mistral, etc. — but that flexibility comes at a cost: the model is not running in an environment that fully unleashes its agentic power.

A superficial answer (as Gemini put it) goes like this: IDEs often use tricks to summarize or compress the context — both the codebase and the chat history — to save cost and latency. While that works for short edits or completions, it sometimes causes the model to miss small but crucial details — an existing helper function, a subtle bug, or a variable defined two files away. IDE workflows are optimized for quick interaction, not for deep reasoning or sustained task execution. The agent’s “view” is constrained to what’s inside the editor window, which limits autonomy and the ability to use the terminal or filesystem freely.

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Claude Code CLI, on the other hand, was built by Anthropic for Anthropic’s models. It’s designed from the ground up to maximize agentic potential — to let the model think long, act across multiple steps, and execute complex refactors or structured workflows. It can directly run shell commands, access the environment without layers of abstraction, and sustain long reasoning chains.

That’s why the same Sonnet 4.5 “feels smarter” inside Claude Code CLI — it’s not that the LLM is better, it’s that the wrapper gives it more freedom to act as an agent.

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Still, this explanation feels shallow to me.
AI itself can’t really explain why or how the wrapper design creates such a gap in depth and reasoning. These internals — context management, agent orchestration, tool integration — are probably guarded know-how, the kind of system-level design Anthropic or OpenAI won’t disclose publicly.

Since I’m building agents myself, I need to figure this out — not just at the surface level, but at the architectural one. I want to understand how context caching, tool access, memory persistence, and prompt orchestration interplay to produce this “depth of thinking” effect.

AI has limits because it can only infer from what it’s trained on. But that’s where human intelligence still wins. With curiosity, reasoning, and experimentation — and with AI’s own inferencing power — I’m optimistic that I could crack this layer and build even better, smarter agents.