This paper investigates the magnitude of the jump component to total price variance in the Chinese stock market based on the highest resolution data. We apply the newly proposed jump test for semi-martingale contaminated by microstructure noise based on the truncated pre-averaging bi-power estimation. Theoretically, we prove that such test achieves satisfactory asymptotic size and power. The universal threshold technique can also be adopted to avoid spurious detections and the Monte Carlo simulations show reasonable performance of the test in noisy setting. The empirical results imply that jump variation is an order of magnitude smaller than typical estimates found in the existing literature from different angles, and the further empirical results also support these findings.