Software vulnerabilities are frequently discovered and exploited, posing serious risks to software systems and society. Fuzzing has become a key technique for automatically detecting these vulnerabilities by generating unexpected inputs. In recent years, fuzzing has been integrated into continuous integration workflows, enabling short and frequent testing cycles. Despite its widespread adoption, prior research has not examined whether the effectiveness of continuous fuzzing varies across programming languages. This study conducts a large-scale cross-language analysis to elucidate how fuzzing bug characteristics and detection efficiency differ among languages. We analyze 60,649 fuzzing bugs and 992,530 builds from 551 OSS-Fuzz projects categorized by their primary language. Our findings reveal that (i) C++ and Rust exhibit higher fuzzing bug detection frequencies than other languages, (ii) Rust and Python show relatively low vulnerability ratios but tend to expose more critical vulnerabilities, (iii) crash types show biases across languages and unreproducible bugs are more frequent in Go but rare in Rust, and (iv) Python attains higher patch coverage but suffers from longer time-to-detection. These results demonstrate that fuzzing behavior and effectiveness are strongly shaped by programming language design, providing new insights for language-aware fuzzing strategies and tool development.