Comment by dailykoder
1 year ago
I am mostly only an LLM user with technical background. I don't have much in-depth knowledge. So I have questions about this take:
>the output token allowance has been increased dramatically—to 32,768 for o1-preview and 65,536 for the supposedly smaller o1-mini!
So the text says reasoning and output tokens are the same, as in you pay for both. But does the increase say that it can actually do more, or does it just mean it is able to output more text?
Because by now I am just bored of GPT4o output, because I don't have the time to read through a multi-paragraph text that explains to me stuff that I already know, when I only want to have a short, technical answer. But maybe that's just what it can't do, give exact answers. I am still not convinced by AI.
I included that note because output limits are a personal interest of mine.
Until recently most models capped out at around 4,000 tokens of output, even as they grew to handle 100,000 or even a million input tokens.
For most use-cases this is completely fine - but there are some edge-cases that I care about. One is translation - if you feed in a 100,000 token document in English and ask for it to be translated to German you want about 100,000 tokens of output, rather than a summary.
The second is structured data extraction: I like being able to feed in large quantities of unstructured text (or images) and get back structured JSON/CSV. This can be limited by low output token counts.
Sure, your cases are perfectly reasonable. I just wish the LLMs had a "feel" about when to output long or short text. Always thinking about adding something like "be as concise as possible" is kinda tedious