LiveCodeBench, a benchmark for evaluating code generation models in Python, has been extended to support multiple programming languages through the Multi-LCB project, generating 33 upvotes on HuggingFace. The ability to benchmark across languages addresses a structural gap in AI code evaluation. Most benchmarks remain Python-centric, creating blind spots for Java, C++, Go, and TypeScript ecosystems where production workloads concentrate. Fair cross-language comparison requires standardized, language-agnostic evaluation criteria—this extension provides that foundation, enabling clearer assessment of model performance where it actually matters operationally. For builders: multi-language benchmarks reduce the friction of validating models against real deployment targets. Teams no longer need custom evaluation scaffolding per language; standardized results become comparable across codebases. For operators: this shifts resource allocation away from one-off language-specific testing toward unified model evaluation pipelines. Infrastructure investment in benchmark infrastructure becomes language-aware rather than language-specific, lowering per-language validation costs.