---
title: "LLM Leaderboards"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# LLM Leaderboards

**[Chatbot Arena Leaderboard](https://lmarena.ai/?leaderboard)**

**[GAIA Leaderboard](https://huggingface.co/spaces/gaia-benchmark/leaderboard)**

**[Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/)**

**[Berkeley Function-Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html)**

**[Berkeley Function-Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html)**

**[Creative Writing: Emotional Intelligence Benchmarks for LLMs](https://eqbench.com/creative_writing.html)**

**[ArtificialAnalysis/Video-Generation-Arena-Leaderboard](https://huggingface.co/spaces/ArtificialAnalysis/Video-Generation-Arena-Leaderboard)**

**LLM leaderboards** are platforms that evaluate and rank Large Language Models (LLMs) based on their performance across various benchmarks and tasks. These leaderboards provide insights into the capabilities of different models, assisting researchers and practitioners in selecting the most suitable models for their specific applications.


**Prominent LLM Leaderboards:**

1. **[Chatbot Arena Leaderboard](https://lmarena.ai/?leaderboard)**: This leaderboard evaluates LLMs using a crowdsourced, randomized battle platform, incorporating over 2.3 million user votes to compute Elo ratings. It also employs benchmarks such as MT-Bench and MMLU to measure multitask accuracy across various tasks. 

[The UC Berkeley Project That Is the AI Industry’s Obsession](https://www.wsj.com/tech/ai/the-uc-berkeley-project-that-is-the-ai-industrys-obsession-bc68b3e3)

Chatbot Arena, developed by UC Berkeley graduate students Anastasios Angelopoulos and Wei-Lin Chiang, has become a focal point in the AI industry. Launched in April 2023, this platform allows users to pose questions to various AI models anonymously and rate their responses. This head-to-head comparison generates a leaderboard featuring major AI systems from companies like OpenAI, Google, Meta, and various startups. 

The platform's popularity stems from its user-driven evaluation method, which offers an alternative to traditional AI benchmarks that may no longer effectively measure performance. By focusing on user preferences, Chatbot Arena provides insights into how different AI models perform in real-world interactions. Since its inception, the platform has expanded to rank over 170 models, accumulating millions of votes. 

Despite its success, Angelopoulos and Chiang manage Chatbot Arena without financial compensation, balancing this endeavor with their academic responsibilities. The platform has also become a testing ground for new technologies from leading AI companies, further solidifying its significance in the industry. 

However, some experts caution that while Chatbot Arena offers valuable insights, it may not serve as the definitive benchmark for AI performance. The platform's reliance on user interactions introduces variables that can affect the consistency and objectivity of evaluations. 

In summary, Chatbot Arena represents a significant shift in AI model evaluation by emphasizing user preferences and real-world performance. Its influence continues to grow, impacting how AI systems are assessed and developed across the industry. 

2. **[Open LLM Leaderboard by Hugging Face](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)**: This platform tracks and ranks open-source LLMs and chatbots, offering detailed results and queries for each model. It utilizes benchmarks like the Eleuther AI LM Evaluation Harness to assess model performance. 

## Important Note:

The **Chatbot Arena Leaderboard** is a platform that evaluates and ranks large language models (LLMs) based on user interactions and preferences. It was developed by graduate students at the University of California, Berkeley, and has become a significant resource in the AI community. The platform allows users to pose questions to anonymous AI models and rate their responses, generating a leaderboard that includes major players like OpenAI, Google, and Meta, as well as startups. 

The leaderboard is accessible through multiple domains, including **lmarena.ai** and **openlm.ai**. Both URLs direct users to the same Chatbot Arena platform, offering identical features and content. The use of different domain names does not indicate separate platforms; rather, they serve as alternative access points to the same resource.

In summary, there is no difference between the Chatbot Arena Leaderboard accessed via lmarena.ai and openlm.ai. They are simply different domain names leading to the same platform, providing users with flexibility in how they access the service. 

**Most Referenced Leaderboard:**

The **Chatbot Arena Leaderboard** by OpenLM.ai has garnered significant attention within the AI community, offering a unique platform for evaluating and ranking large language models (LLMs) through direct, head-to-head comparisons. Since its launch in April 2023, the platform has expanded to rank over 170 models, accumulating more than two million votes, and has become a focal point for AI developers and researchers. 

This approach has attracted substantial interest from major AI companies, with models from OpenAI, Google, and Meta being tested on the platform, sometimes even before their public release. The leaderboard's influence is evident, as AI professionals closely monitor rankings to assess the performance of various models. 

In comparison, the **Open LLM Leaderboard** by Hugging Face evaluates and ranks open-source LLMs and chatbots, providing reproducible scores to distinguish actual progress in the field. It has attracted around 300,000 community members who use and collaborate on it monthly through submissions and discussions. 

While both platforms are widely utilized, the Chatbot Arena Leaderboard's interactive, user-driven evaluation method has made it a particularly popular and influential tool for assessing LLM performance.

## You Can Submit Your Model on Chatbot Arena Leaderboard

Evaluating models on the **Chatbot Arena Leaderboard** involves a systematic process of submitting your language model for assessment and understanding the evaluation metrics employed. Here's a detailed guide to navigate this process:

**1. Understanding Chatbot Arena**

Chatbot Arena is an open platform developed by researchers at UC Berkeley's SkyLab and LMSYS. It facilitates the evaluation of Large Language Models (LLMs) through direct, pairwise comparisons, allowing users to interact with anonymous AI models and rate their responses. This crowdsourced approach aggregates user preferences to rank models on the leaderboard. 

**2. Preparing Your Model for Submission**

Before submitting your model, ensure it meets the following criteria:

- **Instruction-Tuning**: Your model should be fine-tuned to follow instructions effectively, as the evaluation focuses on instruction-following capabilities.

- **API Endpoint**: Provide a stable API endpoint that the Chatbot Arena can use to interact with your model.

- **Documentation**: Prepare comprehensive documentation detailing your model's architecture, training data, and any specific features or limitations.

**3. Submission Process**

To submit your model:

- **Contact the Chatbot Arena Team**: Reach out to the administrators through the contact information provided on the [Chatbot Arena website](https://openlm.ai/chatbot-arena/).

- **Provide Necessary Details**: Share your model's API endpoint, documentation, and any other required information as specified by the Chatbot Arena team.

- **Compliance Check**: Ensure your model complies with the platform's guidelines and standards for evaluation.

**4. Evaluation Methodology**

Once submitted, your model undergoes evaluation through the following methodologies:

- **Pairwise Comparisons**: Users are presented with responses from two anonymous models to the same prompt and asked to choose the better response. This process generates Elo ratings, a system widely used in competitive games like chess, to rank the models. 

- **MT-Bench**: A set of challenging multi-turn questions graded by GPT-4 to assess the model's performance across various tasks. 

- **MMLU (5-shot)**: Measures the model’s multitask accuracy across 57 tasks to evaluate its generalization capabilities. 

**5. Monitoring Performance**

After evaluation, your model's performance will be displayed on the [Chatbot Arena Leaderboard](https://openlm.ai/chatbot-arena/). Regularly monitor the leaderboard to assess how your model compares to others and identify areas for improvement.

**6. Iterative Improvement**

Use the insights gained from the leaderboard to refine your model. Consider fine-tuning with additional data, adjusting hyperparameters, or enhancing specific capabilities to improve performance in future evaluations.

**7. Community Engagement**

Engage with the Chatbot Arena community to share experiences, discuss evaluation strategies, and collaborate on best practices for model development and assessment.

By following this guide, you can effectively evaluate your language model on the Chatbot Arena Leaderboard, gaining valuable insights into its performance and areas for enhancement.

For a visual overview of the Chatbot Arena Leaderboard and its evaluation process, you may find the following video helpful:

[Chatbot Arena Leaderboard: Evaluation & Ranking of LLMs!](https://www.youtube.com/watch?v=K_vMibvnDwo)

## [Participate in Evaluating the Models](https://lmarena.ai/)

Participating in the evaluation of Large Language Models (LLMs) through pairwise comparisons on **Chatbot Arena** is a valuable way to contribute to the AI community. Your input helps determine which models perform better in various contexts. Here's a step-by-step guide to get involved:

**1. Access Chatbot Arena**

- Navigate to the [Chatbot Arena platform](https://lmarena.ai/).

**2. Initiate a Comparison Session**

- On the homepage, select the option to start a new chat or comparison.

**3. Engage with the Models**

- You'll be presented with two anonymous AI models side by side.
- Enter a prompt or question into the input field.
- Both models will generate responses to your input.

**4. Evaluate the Responses**

- Carefully read each response, considering factors like relevance, coherence, accuracy, and completeness.
- Decide which response you prefer based on these criteria.

**5. Cast Your Vote**

- Click on the button corresponding to the model whose response you found better.
- After voting, the identities of the models are revealed, allowing you to see which models you evaluated.

**6. Continue the Process**

- You can repeat the process with new prompts to evaluate additional model pairs.
- Each comparison you make contributes to the overall assessment and ranking of the models.

**Important Considerations**

- **Impartiality**: Strive to evaluate responses objectively, focusing solely on the quality of the content without bias toward any model.
- **Diversity of Prompts**: Use a variety of prompts, including different topics and complexities, to provide comprehensive evaluations.
- **Consistency**: Apply the same evaluation criteria across all comparisons to maintain consistency in your assessments.

**Impact of Your Participation**

Your evaluations play a crucial role in the Chatbot Arena's ranking system. By providing diverse and unbiased feedback, you help create a more accurate and representative assessment of each model's performance. This collective effort enhances the development and refinement of LLMs, contributing to advancements in AI technology.

For more detailed information on the Chatbot Arena's methodology and its significance in LLM evaluation, refer to the paper "Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference" ([arxiv.org](https://arxiv.org/abs/2403.04132)).

By following this guide, you can effectively participate in the pairwise comparison process on Chatbot Arena, aiding in the evaluation and improvement of AI language models.
