Foundational models can be a useful tool for many applications, however, accurate simulations of high temperature MOFs remains a challenging application. Here we demonstrated for our high temperature dataset of CALF-20 and ZIF-8 the foundational models, MACE-MP-0, ORB-d3-v2 and SevenNet-0, were all significantly above quantum accuracy across energy, force and stress metrics. We found finetuning can significantly improve the accuracy of these models towards our application. Additionally, using average zinc coordination number and bond length sampling methods, these models can be trained more efficiently and more accurately than random sampling as biased sampling can better probe extremities in behavior. For example, coordination number sampling could more accurately describe the formation of gaseous CALF-20. It was also found that training models "from scratch" leads to a more accurate model than a finetuned model. A "from scratch" model trained using coordination number sampling was shown to replicate DFT RDFs across 300, 1000 and 2000 K for CALF-20 and ZIF-8, suggesting that the models can accurately predict how the zinc environment evolves at high temperature. This model was then used to simulate a 1 ns quench of ZIF-8 at 1750 K and CALF 20 at 1750 K. Throughout ZIF-8 pyrolysis, we observed significant coordination changes for zinc sites and a lack of complete linker decomposition leading to recovery of a network structure upon quenching. The CALF-20 pyrolysis simulation showed oxalate linker decomposition which formed CO 2 and quenching created a new four coordinate species.