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title: Official Benchmarks Leaderboard 2026
emoji: π
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 5.50.0
app_file: app.py
pinned: false
---
# π Official Benchmarks Leaderboard 2026
A unified leaderboard for **11 official HuggingFace benchmarks**. Compare AI models across math, coding, knowledge, vision, agent, and language tasks.
## β¨ Features
- π **11 Official Benchmarks**: GSM8K, MMLU-Pro, GPQA, HLE, SWE-bench, AIME, HMMT, and more
- ποΈ **Quick Filters**: One-click presets for model sizes and benchmark categories
- π **Interactive Search**: Filter by model name or provider
- π **Size Range Slider**: Filter models by parameter count (0-1100B+)
- π― **Category Selection**: Choose specific benchmark categories to display
- π₯ **Export CSV**: Download filtered leaderboard data
- π **Sortable Columns**: Click any header to sort the table
- π¨ **Modern Design**: Clean, responsive interface with provider logos
## π― Benchmarks Included
### π Math
- **GSM8K**: Grade School Math (8.5K problems)
- **AIME 2026**: American Invitational Mathematics Examination
- **HMMT 2026**: Harvard-MIT Mathematics Tournament
### π§ Knowledge
- **MMLU-Pro**: Massive Multi-task Language Understanding
- **GPQA Diamond**: PhD-level expert questions
- **HLE**: Humanity's Last Exam
### π» Coding
- **SWE-bench Verified**: Real-world software engineering tasks
- **SWE-bench Pro**: Advanced software engineering challenges
### ποΈ Vision
- **olmOCR**: OCR evaluation benchmark
### π€ Agent
- **Terminal-Bench 2.0**: Terminal command understanding
### π¬ Language
- **EvasionBench**: Language understanding challenges
## π Quick Start
The leaderboard loads automatically from the HuggingFace dataset: `OpenEvals/leaderboard-data`
**Quick Filters:**
- πΉ **Small (<10B)**, πΈ **Medium (10-100B)**, πΆ **Large (100B+)** - Filter by model size
- π» **Coding**, π§ **Knowledge**, π **Math**, etc. - Show only specific categories
## π Data Source
**Dataset**: [OpenEvals/leaderboard-data](https://huggingface.co/datasets/OpenEvals/leaderboard-data)
All scores are aggregated from official HuggingFace benchmark leaderboards. The dataset is updated regularly with the latest model evaluations.
## π» Local Development
```bash
# Install dependencies
pip install -r requirements.txt
# Run the app
python app.py
```
## π Project Structure
```
.
βββ app.py # Main Gradio application
βββ utils/
β βββ data_loader.py # Load data from HuggingFace dataset
β βββ filters.py # Filter and search logic
β βββ formatters.py # Data formatting utilities
β βββ html_generator.py # Generate HTML leaderboard table
βββ static/
β βββ sortTable.js # Client-side table sorting
βββ data/
β βββ provider_logos.json # Provider avatar URLs
βββ requirements.txt # Python dependencies
```
## π§ Technologies
- **Gradio 5.50.0**: Interactive web interface
- **Datasets**: HuggingFace datasets library
- **Pandas**: Data manipulation
- **RangeSlider**: Custom Gradio component for size filtering
## π License
Data is sourced from official HuggingFace benchmarks. Please refer to individual benchmark pages for specific licensing information.
---
Made with β€οΈ by the Benchmarks Team
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