BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution

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Crowdsourced model evaluation platforms, such as Chatbot Arena, enable real-time evaluation from human perspectives to assess the quality of model responses. In the coding domain, manually examining the quality of LLM-generated content is extremely challenging, as it requires understanding long chunks of raw code and deliberatively simulating code execution. To this end, we introduce BigCodeArena, an open human evaluation platform for code generation back-ended with a comprehensive and on-the-fly execution environment. Built on top of Chatbot Arena, BigCodeArena features to enable the execution of LLM-generated code and allow humans to interact with the execution process and outcomes. We collected over 14K raw code-centric conversation sessions across 10 widely used LLMs, spanning 10 programming languages and 8 types of execution environments. Among these conversations, we identify more than 4.7K multi-turn samples with pairwise human preference. Further analysis uncovers the underexplored preferences of LLMs in fine-grained domains characterized by tasks, languages, and frameworks. To systematically examine code understanding and generation capabilities of frontier LLMs, we curate two benchmarks based on the collected data, namely BigCodeReward and AutoCodeArena. For BigCodeReward, we postprocess the 4.7K conversations and evaluate the consistency between reward models and human preference. The evaluation shows that most LLMs have superior performance in judging coding preferences when the execution results are given. Inspired by the findings, we propose AutoCodeArena, an automatic Elo rating benchmark designed to assess the coding quality of LLMs without humans. We find that proprietary LLMs like GPT-5, Claude-Sonnet-4, and Claude-Opus-4 still lead the performance in code generation among the recent emerging models. To democratize transparent evaluation of code generation in the wild, we aim to establish BigCodeArena as a long-term project.