# BigCodeBench Leaderboard Deep Dive

## What Is It?

**BigCodeBench** is the next-generation benchmark for evaluating code generation capabilities of LLMs. Unlike HumanEval (which is now "saturated"), BigCodeBench tests realistic programming with 1,140 tasks requiring 139 Python libraries. The leaderboard tracks 163+ models.

**Leaderboard:** [huggingface.co/spaces/bigcode/bigcodebench-leaderboard](https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard)

**Repository:** [bigcode-project/bigcodebench](https://github.com/bigcode-project/bigcodebench)

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## The Problem It Solves

Traditional benchmarks (HumanEval, MBPP) are:
- **Too simple** - Self-contained algorithmic puzzles
- **Saturated** - Top models score 90%+, can't differentiate
- **Unrealistic** - Don't reflect actual development work
- **Contaminated** - Training data may include benchmark solutions

BigCodeBench provides:
- **Complex, real-world tasks** requiring multiple libraries
- **Better differentiation** (best model: 61%, not 95%)
- **Realistic evaluation** of production coding capabilities
- **Rigorous testing** (99% branch coverage per task)

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## Key Statistics

| Metric | Value |
|--------|-------|
| **Total Tasks** | 1,140 |
| **Python Libraries** | 139 |
| **Function Calls** | 723 unique |
| **Domains** | 7 |
| **Models Evaluated** | 163+ |
| **Best Score (GPT-4o)** | 61.1% |
| **Human Performance** | 97% |

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## How It Differs from Other Benchmarks

| Aspect | HumanEval | MBPP | BigCodeBench | SWE-bench |
|--------|-----------|------|--------------|-----------|
| **Tasks** | 164 | ~378 | 1,140 | Real issues |
| **Library Usage** | Minimal | Minimal | 139 libraries | Production |
| **Complexity** | Low | Low | High | Very high |
| **Saturated?** | Yes | Yes | No | No |
| **Real-world** | Poor | Poor | High | Very high |

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## Current Top Models

### As of January 2025

**Closed-Source:**
1. **GPT-4o** (OpenAI) - 61.1% Complete, 51.1% Instruct
2. **GPT-4 Turbo** - Second tier

**Open-Source:**
1. **Qwen2.5-Coder** (Alibaba) - Best open model
2. **DeepSeek-Coder-V2** - Strong performance
3. **StarCoder2** - Competitive at various sizes

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## Critical Finding

### The Human-AI Gap

| Entity | Score |
|--------|-------|
| **GPT-4o (Best)** | 61.1% |
| **Human Performance** | 97% |
| **Gap** | 36% |

Even the best model falls dramatically short of human capability on realistic programming tasks.

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## Two Evaluation Scenarios

### 1. BigCodeBench-Complete
- Code completion from structured docstrings
- More favorable for models
- Baseline scenario

### 2. BigCodeBench-Instruct
- Code generation from natural language
- More challenging (8.5% average drop)
- Tests instruction-following ability

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## Benchmark Variants

| Variant | Tasks | Purpose | Evaluation Time |
|---------|-------|---------|-----------------|
| **Full** | 1,140 | Comprehensive | 25-30 min |
| **Hard** | 148 | Challenging subset | 15-20 min |
| **Lite** | Planned | Minimal subset | TBD |

### BigCodeBench-Hard Selection Criteria
- At least 2 library imports required
- Balanced domain distribution
- Excludes easy tasks
- Best for resource-constrained evaluation

---

## Metrics Used

### Primary: Pass@1 (Greedy)
- Percentage solved with first attempt
- Most practical for production use

### Calibrated Pass@1
- Adds missing imports/setup code
- Reveals when models skip preambles
- More accurate real-world reflection

### Elo Rating
- Chess-inspired ranking system
- Nuanced model comparisons
- Bootstrap validation (500 iterations)

---

## 7 Domains Covered

1. **Computation** - Mathematical operations
2. **Cryptography** - Security operations
3. **Network** - Web/API interactions
4. **Data Processing** - Data manipulation
5. **General Programming** - Common tasks
6. **Algorithms** - Classic CS problems
7. **Symbolic Reasoning** - Logic operations

---

## The BigCode Project

BigCodeBench is part of **BigCode**, an open scientific collaboration by Hugging Face and ServiceNow:

### Related Outputs
- **The Stack** - 6.4 TB source code dataset
- **The Stack v2** - Enhanced dataset
- **StarCoder Models** - 3B, 7B, 15B parameters
- **Evaluation Harness** - Standardized testing

### Mission
Open and responsible development of LLMs for code, with emphasis on:
- Ethical training data
- Developer consent
- Attribution for generated code

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## Key Insights

### 1. Instruction Following is Harder
8.5% performance drop from Complete → Instruct shows models struggle with natural language descriptions.

### 2. Multi-Library Reasoning is Weak
Models struggle with compositional function calls across multiple libraries.

### 3. Many Tasks Remain Unsolved
- 149 tasks (Complete): 0% solved by any model
- 278 tasks (Instruct): 0% solved by any model

### 4. Domain Performance Varies
- **Strong:** Computation, cryptography, general programming
- **Weak:** Network tools, domain-specific libraries

---

## Relevance for Tool Selection

### When Evaluating AI Coding Tools

1. **Reality Check:** BigCodeBench scores reflect actual capability
2. **61% Pass@1:** Plan for human review of ~40% of outputs
3. **Model Comparison:** Use scores for objective comparison
4. **Task Matching:** Consider domain-specific performance

### Selection Matrix

| Priority | Consideration |
|----------|---------------|
| **General coding** | Use overall Pass@1 score |
| **Domain-specific** | Check domain breakdown |
| **Cost-sensitive** | Compare open vs closed models |
| **Production use** | Account for Instruct score |

---

## How to Submit Models

### 1. Install BigCodeBench

```bash
pip install bigcodebench --upgrade
```

### 2. Generate Samples

```bash
bigcodebench generate \
  --model YOUR_MODEL \
  --subset complete \
  --backend vllm
```

### 3. Evaluate

```bash
bigcodebench evaluate \
  --samples samples.jsonl
```

### 4. Submit

Email to `terry.zhuo@monash.edu`:
- Generated code samples
- Execution results (JSON with Pass@k scores)

### Leaderboard Indicators
- ✨ = Chat setting evaluation
- 💚 = Open weights and data
- 💙 = Open weights, proprietary base

---

## Practical Takeaways

### For Developers

1. **Set expectations:** Even best models need ~40% human oversight
2. **Clear instructions:** Formal specs → better results than natural language
3. **Simple dependencies:** Single-library tasks more reliable
4. **Test everything:** AI-generated code needs comprehensive testing

### For Organizations

1. **Benchmark scores matter:** Use BigCodeBench for vendor evaluation
2. **Update regularly:** Model landscape changes quarterly
3. **Cost vs performance:** Open models are catching up
4. **Domain fit:** Match model strengths to your use case

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## Resources

- **Leaderboard:** https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard
- **Repository:** https://github.com/bigcode-project/bigcodebench
- **Paper:** ICLR 2025 publication
- **BigCode Project:** https://www.bigcode-project.org

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*BigCodeBench is the new standard for evaluating code LLMs. If you're choosing an AI coding tool, these scores tell you what to actually expect in production - not the inflated numbers from saturated benchmarks.*
