LeverUp has upgraded to Pyth Pro — the institutional tier of the Pyth oracle network. We have the data to show what that means. Measured across 370 parallel samples on Monad mainnet, price staleness dropped from 1.676 seconds on Pyth Core down to 0.086 seconds on Pyth Pro. That's a roughly 19.5× improvement in measured feed freshness.

This episode walks through why oracle staleness is the largest hidden cost most traders never measure, how the methodology was constructed (side-by-side sampling over a live 17-minute window), and what the specific findings mean for opens, closes, and liquidation checks. It also addresses the key consistency question: did Pyth Pro maintain its lead across all segments of the sampling window, not just on average? And the directional bias question: is Pro simply printing higher prices more often, or tracking the ...