Weak supervision (WS) is a popular approach for label-efficient learning, leveraging diverse sources of noisy but inexpensive weak labels to automatically annotate training data. Despite heavy usage, the value of WS is challenging to benchmark due to its complexity: the knobs involved include data sources, labeling functions (LFs), aggregation techniques, called label models (LMs), and end model pipelines. Existing evaluation suites tend to be limited, focusing on particular components or specialized use cases, or relying on simplistic benchmark datasets with poor LFs, producing insights that may not generalize to real-world settings. We address these by introducing a new benchmark, BoxWRENCH, designed to more accurately reflect real-world usage of WS. This benchmark features (1) higher class cardinality and imbalance, (2) substantial domain expertise requirements, and (3) linguistic variations found in parallel corpora. We improve upon existing benchmark LFs using a rigorous procedure aimed at mimicking real-world settings. In contrast to existing WS benchmarks, we show that in many practical settings supervised learning requires substantial amounts of labeled data to match WS performance.