Comment by akst

I mean not too surprising, it's a specialised model that trained on relevant quantitive/qualitative inputs.

LLMs are very general and flexible, by all means they can do well on non-text based specialised problems, but it also does well because their harnesses know how to defer to the right tools a lot of the time, and produces good answers from inspecting the output. Like if you ask Codex/Claude Code to transcribe a videos, it's going to create a script that uses whisper to extract the transcript not manually process the binary. If you ask it for demographic information it'll pull census data and write a script which produces the summary stats and report it back to you (I imagine much of the recent math stuff relied on this).

Sometimes it'll pull a number from its training data but the strength of an LLM is its ability to autonomously seek out the right answer even when it doesn't know the answer initially.

You could definitely use train an LLM to utilise these efficient specialised models, by it may be more cost effective to ask the model to build a special purpose application with a new UI over the top of the efficient specialised model.