BigCodeBench

One-Minute Overview#

BigCodeBench is an easy-to-use benchmark for solving practical and challenging tasks via code, designed to evaluate the true programming capabilities of large language models (LLMs) in more realistic settings. This benchmark focuses on function-level code generation tasks with much more complex instructions and diverse function calls than traditional tests, making it ideal for researchers and developers to assess LLM programming performance.

Core Value: Provides precise LLM programming evaluation and rankings while open-sourcing pre-generated sample data to accelerate code intelligence research.

Quick Start#

Installation Difficulty: Medium - Requires Python environment, several optional dependencies, and API key configuration

# Basic installation
pip install bigcodebench --upgrade

# Recommended for improved code generation
pip install packaging ninja
pip install flash-attn --no-build-isolation

Is this suitable for my scenario?

• ✅ Researchers needing to evaluate LLM code generation capabilities
• ✅ Developers wanting to understand current LLM programming performance rankings
• ✅ Those who want to use pre-generated samples for code intelligence research
• ❌ Offline-only evaluation environments (some features require internet connection)
• ❌ Researchers without API keys or unwilling to use cloud services

Core Capabilities#

1. Dual-Mode Evaluation - Complete and Instruct#

• Offers Complete mode (code completion based on comprehensive docstrings) and Instruct mode (code generation based on natural language instructions) User Value: Comprehensively evaluates LLM performance across different prompting styles, supporting both base and chat models

2. Precise Benchmarking and Ranking System#

• Generates LLM leaderboards through rigorous evaluation processes, showing before-and-after performance comparisons User Value: Provides reliable LLM programming capability comparison data to help researchers select the most suitable models

3. Pre-Generated Sample Datasets#

• Open-sources pre-generated samples from various LLMs on the full set, eliminating the need to re-run expensive benchmarks User Value: Significantly reduces research costs and accelerates innovation in the code intelligence field

4. Multiple Backend Support#

• Supports vllm, openai, anthropic, google, mistral, hf and other backends for model inference and evaluation User Value: Flexible adaptation to different research environments and resource constraints

Tech Stack and Integration#

Development Language: Python Key Dependencies: PyTorch, Transformers, vLLM, flash-attn (optional, recommended) Integration Method: Command-line tool / API / Python package

Ecosystem and Extensions#

• Benchmark Extensions: Offers BigCodeBench-Hard subset with 148 more challenging tasks aligned with real-world programming scenarios
• Open Evaluation Platform: BigCodeArena provides 100% free evaluation using the latest frontier models
• Community Leaderboard: Public leaderboard on Hugging Face with real-time code execution sessions

Maintenance Status#

• Development Activity: Very active, with regular new releases and frequent feature updates
• Recent Updates: Released v0.2.2.dev2 in January 2025, evaluating 163 models
• Community Adoption: Widely adopted and trusted by multiple major LLM teams including Meta AI, DeepSeek, Alibaba Qwen, Amazon AWS AI, etc.

Commercial and Licensing#

License: Not explicitly specified (not mentioned in README)

• ✅ Commercial: Likely permitted (used by multiple commercial AI teams)
• ✅ Modifications: Likely permitted (open-source nature)
• ⚠️ Restrictions: Specific license limitations not clearly stated

Documentation and Learning Resources#

• Documentation Quality: Comprehensive
• Official Documentation: README Documentation
• Example Code: Provides various command-line usage examples and backend configuration instructions
• Research Paper: arXiv Paper