Paper 2606.20517
Multi-LCB: Extending LiveCodeBench to Multiple Programming Languages
- Published
- Jun 2026
- Research lab
- Independent
- Citations
- 1
- GitHub
- 27 stars
01 In brief
Summary
Multi-LCB is a benchmark extending LiveCodeBench (LCB) to twelve programming languages (C++, C#, Python, Java, Rust, Go, TypeScript, JavaScript, Ruby, PHP, Kotlin, Scala) to evaluate LLM code generation beyond Python.
It converts LCB's Python tasks into a unified STDIN/STDOUT format, preserving contamination controls and evaluation protocol, and automatically tracks future LCB updates.
The authors evaluated 24 LLMs, finding substantial performance gaps across languages, evidence of Python overfitting, and language-specific contamination.
Python consistently outperformed other languages, with Scala trailing.
The benchmark aims to address LCB's Python-only limitation and provide a rigorous multi-language evaluation resource.
02 From the paper
Abstract
LiveCodeBench (LCB) has recently become a widely adopted benchmark for evaluating large language models (LLMs) on code-generation tasks. By curating competitive programming problems, constantly adding fresh problems to the set, and filtering them by release dates, LCB provides contamination-aware evaluation and offers a holistic view of coding capability. However, LCB remains restricted to Python, leaving open the question of whether LLMs can generalize across the diverse programming languages required in real-world software engineering. We introduce Multi-LCB, a benchmark for evaluating LLMs across twelve programming languages, including Python. Multi-LCB transforms Python tasks from the LCB dataset into equivalent tasks in other languages while preserving LCB's contamination controls and evaluation protocol. Because it is fully compatible with the original LCB format, Multi-LCB will automatically track future LCB updates, enabling systematic assessment of cross-language code generation competence and requiring models to sustain performance well beyond Python. We evaluated 24 LLMs for instruction and reasoning on Multi-LCB, uncovering evidence of Python overfitting, language-specific contamination, and substantial disparities in multilingual performance. Our results establish Multi-LCB as a rigorous new benchmark for multi-programming-language code evaluation, directly addressing LCB's primary limitation and exposing critical gaps in current LLM capabilities.