Large models have advanced in processing long input sequences, but their ability to consistently use information across extended contexts remains a challenge. Recent studies highlight a positional bias where models prioritize information at the beginning or end of the input while neglecting the middle, resulting in a U-shaped performance curve but this was limited to English. Whether this bias is universal or shaped by language-specific factors remains unclear. In this work, we investigate positional bias in Bangla, a widely spoken but computationally underrepresented language. To support this, we introduce a novel Bangla benchmark dataset, bnContextQA, specifically designed for long-context comprehension. The dataset comprises of 350 long-context QA instances, each paired with 30 context paragraphs, allowing controlled evaluation of information retrieval at different positions. Using this dataset, we assess the performance of LLMs on Bangla across varying passage positions, providing insights into cross-linguistic positional effects. The bnContextQA dataset is publicly available at https://github.com/labiba02/bnContextQA.git to support future research on long-context understanding in Bangla and multilingual LLMs.