GPT-4o API vs ChatGPT Web – Inconsistent Performance with PDF Label Extraction

API
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1

Hey everyone,

I’m working with GPT-4o both via the API and in ChatGPT for tasks related to PDFs. I’ve noticed a significant difference in performance between the two. Specifically, ChatGPT (web) is outperforming the API when it comes to intelligently identifying and extracting labels from PDFs that contain a lot of unstructured or random data.

My goal is to extract meaningful labels and map them to form widgets in the PDF. This is necessary because the widget field names are often generic, like “Text Field 1”, and don’t offer any context. ChatGPT handles this quite well, inferring logical labels and relationships, but the API responses are not nearly as accurate or useful.

Has anyone else experienced this kind of discrepancy between the API and ChatGPT web interface?

Also, I’d appreciate any best practices or tips for working with PDFs and form fields in this context.

Thanks!

2

It has absolutely happened to me (and many others).

The thing to remember here is that, you are comparing ChatGPT - which is a fairly advanced tool, to a model that the tool uses.

Here are ways forward that worked for me:

  • Change your model to the same ChatGPT model → chatgpt-4o-latest
  • You have to prompt your API with more context, more instructions than what you would do with ChatGPT
  • You could generate an output on ChatGPT, and then ask it to generate a reverse engineered prompt. Use this in your API prompt.
  • Finally, you could also try using the API call twice, once to extract everything, and then another (say o3 mini) to reason and provide the output.

Hope this helps.