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.