Open LLM Leaderboard

What is Open LLM Leaderboard?

The Open LLM Leaderboard, launched by Hugging Face, is an authoritative leaderboard designed to transparently evaluate and rank various open-source large language models (LLMs). Through a series of standardized benchmarks such as ARC, HellaSwag, MMLU, and TruthfulQA, it provides developers, researchers, and enterprises with a fair and reproducible performance comparison platform, helping users quickly understand the latest advancements and capability boundaries of current open-source models.

Who is it suitable for?

• AI researchers and developers need to understand the performance differences between different open-source LLMs to provide data support for model selection, optimization, or the development of new models.
• Enterprise technology decision-makers : They want to assess the potential of open-source LLMs in specific application scenarios to assist in technology selection and strategic planning.
• AI enthusiasts and learners : interested in large-scale language model technology and eager to keep track of industry trends and model performance.
• Data scientists and engineers : When building or deploying LLM-based applications, reliable performance data needs to be referenced.

Can it be used? How to use it? (Access Guide)

• Access address : https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
• Regional Availability : Generally accessible globally. In mainland China, the Hugging Face website and its Spaces service are usually accessible, but may occasionally be affected by network fluctuations.
• Registration : No registration is required to view the leaderboard data. To participate in model submissions or use other advanced Hugging Face features, you need to register a free Hugging Face account; phone numbers or regional accounts are usually not required.
• Cost : Completely free. There are no charges for browsing the leaderboards or accessing the data.
• Language : The interface language is English. The data and model names displayed on the leaderboard are primarily in English, but the LLM being evaluated may support multiple languages.

Cross-ecosystem comparison: Open LLM Leaderboard vs. similar products

Dimension	Open LLM Leaderboard	OpenCompass (Shanghai Artificial Intelligence Laboratory)	LMSYS Chatbot Arena
Evaluation methods	Quantitative score ranking is based on automated benchmark tests (such as ARC, HellaSwag, MMLU, TruthfulQA, etc.).	It combines automated benchmarking with human evaluation, with particular emphasis on Chinese language proficiency and multimodal capabilities.	Real-time battle results based on anonymous user voting reflect human preferences.
Model range	The primary focus is on large open-source language models.	It covers large models in Chinese and multiple languages, including open source and some closed source models.	It covers a wide range of open-source and closed-source chat models, with an emphasis on conversational capabilities.
Transparency and Reproducibility	High. Provides detailed evaluation methods, datasets, and code; results are reproducible.	High. Provides a detailed evaluation framework, dataset, and toolchain; results are reproducible.	High. Publicly available match data and model rankings, but human bias is highly subjective.
Focus	Provide objective and standardized open-source LLM performance benchmarks to promote community development.	Establish a comprehensive and authoritative large-scale model evaluation system in Chinese and multiple languages to serve users in China and around the world.	It reflects users' preferences for different chat models in real time, making it highly engaging.
Main users	LLM researchers, developers, and enterprise technology selectors.	Chinese LLM developers, researchers, enterprise users, and groups interested in multilingual models.	Ordinary users and AI enthusiasts want to intuitively experience and compare different chat models.

Recommendations : If you are an LLM researcher or developer and want to evaluate and select open-source models based on objective, reproducible, and automated benchmark tests, Open LLM Leaderboard is your first choice. If you are primarily concerned with the overall performance of large Chinese or multilingual models and need an authoritative evaluation framework, OpenCompass will provide a more comprehensive perspective. And if you value user experience and conversational capabilities more and want to understand the model's popularity through human voting, LMSYS Chatbot Arena offers unique reference value.

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