Large language models (LLMs) have demonstrated strong performance on existing code generation benchmarks. However, these benchmarks are almost entirely based on English prompts. This study examines the cost of prompt language on code generation performance for Turkish, an agglutinative and morphologically rich language. For this purpose, EN-TR CodeBench, a benchmark consisting of 60 problems and 811 unit tests at easy, medium, and hard difficulty levels, was created. Five open-source code LLMs from three model families, one with three different parameter scales, were evaluated under English, Turkish, and Turkish-localized conditions using the pass@1 metric. The results show that the use of Turkish prompts leads to a performance decrease in all five models, and this decrease narrows as model scale increases within the Qwen2.5-Coder family. Analysis according to difficulty level reveals that, for most models, the language-related performance loss increases substantially with problem complexity. Error analysis shows that the performance difference between languages is largely driven by runtime and logic errors, while syntax errors remain rare.

Vision-language models (VLMs), despite their success in optical character recognition (OCR) tasks, are vulnerable to typographic attacks and have a fragile structure for images with multiple text layers. In this study, the DecoyBench dataset was created using the Decoy Font method. The dataset consists of 300 images, each containing text with sharp contour lines superimposed on another text with soft shading. Six recent closed-source models from three different model families were evaluated using this dataset under two different prompting conditions (naive and guided) and at two different resolutions (512 × 512 and 64 × 64). A validation study showed that human participants could read both text layers with high accuracy. In contrast, the models, with most variants and both prompting methods, read the contour text with near-human accuracy at high resolution, but almost never fully extracted the shading text. At low resolution, the contour text could not be read by either the models or humans, while the shading text could be extracted with high accuracy. The findings indicate that the evaluated VLMs exhibit a consistent behavioral limitation when processing typographic structures containing multiple spatial frequency layers.