Neither can many humans, including some very smart ones. Even those who can will usually choose to use a calculator (or spreadsheet or whatever) rather than doing the arithmetic themselves.
1) GPT-5 is advertised as "PhD-level intelligence". So, I take OpenAI (and anyone else who advertises their bots with language like this) at their word about the bot's capabilities and constrain the set of humans I use for comparison to those who also have PhD-level intelligence.
2) Any human who has been introduced to long addition will absolutely be able to compute the sum of two whole numbers of arbitrary length. You may have to provide them a sufficiently strong incentive to actually do it long-hand, but they absolutely are capable because the method is not difficult. I'm fairly certain that most adult humans [0] (regardless of whether or not they have PhD-level intelligence) find the method to be trivial, if tedious.
[0] And many human children!
Of course, if you give me 100 10-digit numbers to add up and let me use a calculator, or pencil and paper, then I will probably get it right.
Same for, say, two 100-digit numbers. (I can probably get that one right without tools if you obligingly print them monospaced and put one of them immediately above the other where I can look at them.)
Anyway, the premise here seems to be simply false. I just gave ChatGPT and Claude (free versions of both; ChatGPT5, whatever specific model it routed my query to, and Sonnet 4) a list of 100 random 10-digit numbers to add up, with a prompt encouraging them to be careful about it but nothing beyond that (e.g., no specific strategies or tools to use), and both of them got the right total. Then I did the same with two 100-digit numbers and both of them got that right too.
Difficulty is the amount of digits, small models struggle with 10 digits numbers, gemini and gpt-5 are very good recent models, gemini start failing before 40 digits, GPT-5 (the one by api, the online chat version is worse and I didn't tested it) can do more than 120 digits (at this point it's pointless to test for more).
Of course, I only ran it once; I can't at all rule out the possibility that sometimes it gets it wrong. But, again, the same is true of humans.
> That’s interesting, you added a tool.
The "tool" in this case, is a memory aid. Because they are computer programs running inside a fairly-ordinary computer, the LLMs have exactly the same sort of tool available to them. I would find a claim that LLMs don't have a free MB or so of RAM to use as scratch space for long addition to be unbelievable.
They do have something a bit like it: their "context window", the amount of input and recently-generated output they get to look at while generating the next token. Claude Sonnet 4 has 1M tokens of context, but e.g. Opus 4.1 has only 200k and I think GPT-5 has 256k. And it doesn't really behave like "scratch space" in any useful sense; e.g., the models can't modify anything once it's there.