Track, rank and evaluate open LLMs and chatbots
Calculate VRAM requirements for LLM models
Submit deepfake detection models for evaluation
Browse and filter machine learning models by category and modality
Display model benchmark results
Calculate GPU requirements for running LLMs
Visualize model performance on function calling tasks
Explore and visualize diverse models
View and submit LLM benchmark evaluations
Create and upload a Hugging Face model card
Compare audio representation models using benchmark results
View and submit LLM benchmark evaluations
View and submit machine learning model evaluations
The Open LLM Leaderboard is a comprehensive tool designed to track, rank, and evaluate open-source Large Language Models (LLMs) and chatbots. It provides a transparent and standardized platform to compare models based on various benchmarks and metrics, helping developers, researchers, and users make informed decisions. By focusing on performance, efficiency, and capabilities, the Leaderboard serves as a go-to resource for understanding the evolution and advancements in the field of LLMs.
What metrics are used to rank LLMs? The Leaderboard uses a variety of metrics, including performance benchmarks, speed, memory usage, and specific task accuracy to ensure a holistic evaluation of each model.
Can I compare custom or non-listed models? Yes, the platform allows users to input custom models for comparison, providing flexibility for researchers and developers working on niche or proprietary LLMs.
How often is the Leaderboard updated? The Leaderboard is updated regularly to reflect new releases and improvements in existing models, ensuring users always have access to the latest information.