It is superhuman in a very specific domain. I didn't use AGI because its definitions are one of two flavors.

One, capable of replacing some large proportion of global gdp (this definition has a lot of obstructions: organizational, bureaucratic, robotic)...

two, difficult to find problems in which average human can solve but model cannot. The problem with this definition is that the distinct nature of intelligence of AI and the broadness of tasks is such that this metric is probably only achievable after AI is already in reality massively superhuman intelligence in aggregate. Compare this with Go AIs which were massively superhuman and often still failing to count ladders correctly--which was also fixed by more scaling.

All in all I avoid the term AGI because for me AGI is comparing average intelligence on broad tasks rel humans and I'm already not sure if it's achieved by current models whereas superhuman research math is clearly not achieved because humans are still making all of progress of new results.