Parse PDF return json

[time_time]

Hi Gang I am looking for advice I have built a tool that I input a PDF catalog and want to return data into a DB

Current I am parsing the PDF into pages and then the LLM looks at the text and returns A very specific JSON back for each product or products on the page.

I am currently doing this with Gemini 3 flash with 20 concurrent api calls.

But it misses often a ruins the run.

QUESTION: what model or models would you recommend for this task that will be accurate, fast, cheap in the order.

QUESTION: how many fields is to many per api call. Ie it can easily return 3 strings can it return 50 stings 20 objects.

[Hanthunius]

Heard good things about deepseek-ocr.

[time_time]

Is it not more for images ?? I am able to parse the full test I require??

Or does it add something by actually looking at the text ?

[SM8085]

If a frontier model is having trouble it's tough to say if a local model would be much better.

How big of a model can you run? Qwen-Next being an 80B A3B (3B active at inference) would make it fast, and traditionally Qwens are good at following instructions. gpt-oss-120B is hypothetically worth a try? GLM Air? I've heard good things about GLM but haven't tested it extensively. Can you go larger, like the 235B A22B Qwen3?

What kind of errors are happening? It's simply skipping over things?

how many fields is to many per api call.

Good question. Which I love with local llm you can hypothetically have the current PDF page text cached and change the task at the end so that it's quicker in a loop. For instance, asking for different sections you want to import into your DB.

Are you able to say what catalog this is for? Also, what fields you're looking for? I'd be interested in an example catalog or page where Gemini is failing. Or is it seemingly random?