GPT-4o mini: advancing cost-efficient intelligence

Post by wrs » Sun, Jul 21, 2024, 8:59 PM UTC

sanmon3186 wrote: post #74

wrs wrote: post #20

The big news for me here is the 16k output token limit. The models keep increasing the input limit to outrageous amounts, but output has been stuck at 4k. I did a project to summarize complex PDF invoices (not “unstructured” data, but “idiosyncratically structured” data, as each vendor has a completely different format). GPT-4o did an amazing job at the extraction of line items, but I had to do a heuristic layer on t…

>I had to do a heuristic layer on top to break up the PDFs into small chunks so the output didn’t overflow How do you stitch the outputs of all chunks without losing the overall context?

Post by razodactyl » Sun, Jul 28, 2024, 5:08 PM UTC

skybrian wrote: post #25

razodactyl wrote: post #11

Earlier quoted context omitted.

At scale you should realise that this is still A LOT of money and the models are considerably reduced in cost so the margin probably works out even better. OpenAI are successful, it's a fact, which means they know what they're doing business wise. (Not bootlicking, just trying to be logical). Think about it this way: Imagine if every email you sent or every online forum post you commented on provided incentive for th…

I’m not sure what you mean and I don’t see how profitability follows from that? Venture-backed companies can lose money for years. Sometimes it pays off in the end, but making predictions about profitability seems hard inside a bubble. Also, some industries like manufacturing solar panels have high market growth but they’re unprofitable for most manufacturers. So I think it remains to be seen if OpenAI knows what the…