# Open LLM Leaderboard

- Category: Detection & evals
- Official site: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- HTML: https://www.88z.ai/en/tools/open-llm-leaderboard
- Markdown: https://www.88z.ai/en/tools/open-llm-leaderboard.md

The Open LLM Leaderboard is an interactive application hosted on Hugging Face that allows users to explore and compare the performance of open-source large language models.

## What is Open LLM Leaderboard?

The Open LLM Leaderboard is an interactive application hosted on Hugging Face that allows users to explore and compare the performance of open-source large language models.It provides a transparent view of how different models perform across a rigorous suite of tests, offering data-driven insights for developers and researchers selecting the right model.

By integrating multiple authoritative evaluation benchmarks, this tool comprehensively measures model capabilities in areas such as instruction following, logical reasoning, mathematical problem-solving, and expert knowledge QA.Users can view real-time rankings and detailed scores directly in the browser without needing to set up complex evaluation environments.

## Key features

- **Multi-Benchmark**：Supports major evaluation suites including IFEval, BBH, MATH, GPQA, MUSR, and MMLU-PRO to comprehensively assess model capabilities.
- **Interactive Lead**：Offers an intuitive interface where users can easily browse model rankings, filter specific models, and view detailed score breakdowns.
- **Open-Source Focu**：Specifically tracks and evaluates open-source large language models, fostering transparent competition and technical progress within the open-source community.
- **Real-Time Data U**：Hosted on Hugging Face Spaces, ensuring the leaderboard reflects the latest model submissions and evaluation results.
- **Zero-Config Acce**：No local deployment or complex environment setup required; access complete evaluation data and comparative analysis directly via a web browser.

## How to use it

1. Visit the Open LLM Leaderboard page on Hugging Face Spaces.
2. Wait for the page to load and view the list of top-ranking open-source large language models.
3. Use the search or filter functions to locate specific model names or architectures.
4. Click on individual model entries to view detailed scores across various benchmarks like IFEval, MATH, and GPQA.
5. Compare models based on overall scores or specific task performance to aid in selection decisions.

## Where it fits

- **Model Selection**：Developers quickly identify open-source models that excel in specific domains (such as math or code) when choosing base models for their projects.
- **Academic Researc**：Researchers use standardized benchmark data to objectively compare the performance gap between newly proposed models and existing state-of-the-art open-source models.
- **Tech Trend Track**：AI enthusiasts and industry observers track the latest developments and breakthroughs in large language model technology within the open-source community through ranking changes.

## Pricing and free quota

This tool is a free public space on Hugging Face; please refer to the official site for specific terms of use.

## Who it is for

- AI Model Developers
- Machine Learning Researchers
- Open Source Community Contributors
- Technical Selection Engineers

## FAQ

### Which evaluation benchmarks are included in the Open LLM Leaderboard?

According to official information, the leaderboard covers tests such as IFEval, BBH, MATH, GPQA, MUSR, and MMLU-PRO.

### Does this leaderboard only include closed-source models?

No, it specifically focuses on evaluating the performance of open-source large language models.

### How can I view the specific scores for a model?

You can click on a specific model in the interactive interface to view its detailed scores and rankings across various benchmarks.

## Tags

LLM Evaluation · Open Source Models · Benchmarks · Hugging Face

Features, pricing and availability follow the official site. Except for 88z.ai’s own API pages, this is a third-party product; 88z.ai only provides a directory introduction.