I think Gemini Flash 1.5 is the best closed-source model for this. Very cheap. Particularly compared to GPT4o-mini, which is priced the same as GPT4 for image input tokens. Performance and speed is excellent. I convert each pdf page to an image and send one request per page to Flash (asynchronously). The prompt asks for markdown output with specific formatting guidelines. For my application (mainly pdf slideshows with less text), the output is better than any of the dedicated tools I tested particularly for equations and tables.
> I convert each pdf page to an image and send one request per page to Flash
Why convert? Flash 1.5 accepts whole PDFs just fine. It will also increase the models response accuracy.
Context: I have found Flash 1.5 is excellent and stable for this kind of use-case. Even at a non-EA price-point it's incredibly cheap, especially when utilizing Batch Prediction Jobs (50% discount!).
In my experience at this point all the flagship multi-modal LLM provide for the same accuracy. I see very little, if any, drift in output between them, especially if you have your prompts dialed.
For the Gemini Flash 1.5 model GCP pricing[0] treats each PDF page an image, so you're looking at pricing per image ($0.00002) + the token count ($0.00001875 / 1k characters) from the base64 string encoding of the entire PDF and the context you provide.
10 page PDF ($0.0002) + ~3,000 tokens of context/base64 ($0.00005625) = $0.00025625
Cut that in half if you utilize Batch Prediction jobs[1] and even at scale you're looking at a rounding error in costs.
For on-going accuracy tracking I take a static proportion of the generations (say 1%, or 10 PDFs for every 1,000) and run them through an evaluation[2] workflow. Depending on how/what you're extracting from the PDFs the eval method is going to change, but I find for "unstructured to structured" use-cases the fulfillment evaluation is a fair test.
Essentially what you're already doing, with one more step :) Get PDF > convert (read: rebuild) to TIFF > convert to PDF.
In my case all documents to be sent to the LLM (PDFs/Images/emails/etc) are already stagged in a file repository as part of a standard storage process. This entails every document being converted into a TIFF (read: rebuilt cleanly) for storage, and then into PDF upon export. This ensures that all docs are correct and don't maintain whatever went into originally creating them. I've found any number of "PDF" documents are not PDF, while others try and enforce some "protection" that makes the LLM not like the DOCS
Cheap for now. One day, once the market shares balance out, the cloud spend will increase. Local LLMs may be important to prioritize for code that may be running after multiple subscription cycles into the future.
Edit: oh you best wrote closed-source model whoops
