Hi everyone,
I’m building a cold-calling research workflow in n8n and I’m running into a confusing issue with the OpenAI (ChatGPT) node that I can’t seem to resolve.
Workflow (very simple)
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Manual Trigger
-
Google Sheets (1 row at a time)
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OpenAI Chat Model node
No extra parsing or transformations.
The problem
I’m using a prompt that works perfectly when I run it directly in the ChatGPT UI (chat.openai.com), but when I use the exact same prompt inside the n8n OpenAI node, the output becomes very generic and full of "N/A" values.
Example
Prompt intent
Research a real business and return structured JSON with:
-
Design project / business name
-
Focus area
-
Social media platform, activity level, and followers
Result in ChatGPT UI (correct, detailed, fact-checked)
{
"Design Project": "7 Plates Cafe - Chicago, IL",
"Focus Area": "Hospitality & Commercial Interior Design",
"Social Media Presence": {
"Platform": "Instagram",
"Activity Level": "High",
"Followers": "Approx. 6.9K+"
}
}
This output is accurate and matches real-world data.
Result in n8n OpenAI node (same prompt)
{
"Design Project": "N/A",
"Focus Area": "Interior Design",
"Social Media Presence": {
"Platform": "Twitter",
"Activity Level": "Low",
"Followers": "N/A"
}
}
This happens consistently across different businesses.
What I’ve already checked
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Tried different models (GPT-4 / GPT-4o)
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Adjusted temperature
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Tested JSON vs text output
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Confirmed prompt content is identical
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No output parsers or extra nodes involved
Question
Is there a known difference in:
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Inference behavior
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Entity resolution
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Or safety defaults
between the ChatGPT UI and the OpenAI API used by n8n, that would cause the model to return conservative "N/A" placeholders unless explicitly instructed otherwise?
If so, is there a recommended way in n8n to:
-
Enable more confident inference, or
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Match ChatGPT UI behavior more closely for research-style prompts?
Any guidance or examples would be greatly appreciated and i cloud really help me out in this siatuation thank you so much🙏