Why your extended coding prompt works fine in ChatGPT (GPT-4o) but struggles in Claude (even with a larger token window)

🧠 Key Differences: Claude 3 vs GPT-4o in Practice

🔍 Why ChatGPT Handles the Same Prompt Better

OpenAI has quietly built very effective compression of prior messages, so it keeps more context without hitting soft ceilings. Claude stores more raw input, which bloats the token count.

🔄 GPT-4o can handle long-running threads with less performance decay.

When Claude gets near its limit, it tends to:
• reject the prompt,
• produce errors,
• or give partial responses.

GPT-4o instead:
• shortens output,
• warns when trimming,
• or optimises on-the-fly.

Especially in long prompts with:
• nested conditionals,
• multi-step tasks,
• or large block references

GPT-4o tends to synthesise more accurately — Claude often fails to generalise or gets stuck in literal repetition.

🧪 Real Prompt Example (Coding Context)

“Here’s a multi-file Python project. Rewrite the data_pipeline.py module to integrate logging, error handling, and a retry mechanism. Keep comments. Return as code only.”

Result:
• ✅ GPT-4o handles it with clean output.
• ⚠️ Claude (Sonnet) may fail, especially if you’ve already shared earlier files in the same thread.

🧩 Summary: It’s Not Just the Token Limit — It’s the Model Engineering

✅ Final Thought

Claude has a larger window, but ChatGPT (GPT-4o) uses its smaller window more efficiently.
That’s why you can push complex prompts further in GPT-4o without hitting walls — especially for coding, long instructions, or iterative threads.