Quantum computing calibration relies heavily on the interpretation of experimental data, with calibration plots serving as a crucial human-readable representation. However, a systematic evaluation of vision-language models' (VLMs) ability to interpret these plots has been lacking. To address this, researchers have introduced QCalEval, a benchmark for assessing VLMs' performance in understanding quantum calibration plots1. This benchmark comprises 243 samples spanning 87 scenario types from 22 experiment families, providing a comprehensive framework for evaluating VLMs. By establishing a standardized evaluation protocol, QCalEval enables the comparison of different VLMs and facilitates the development of more accurate models. This matters to practitioners because it allows them to assess and improve the performance of VLMs in quantum computing applications, ultimately enhancing the reliability and efficiency of quantum calibration processes.