Generative AI Models

Generative AI is becoming increasingly popular across industries and disrupting how businesses operate. With the growing demand for AI-driven solutions, numerous generative AI models are now available. However, determining which model best suits a particular application can be challenging. To address this challenge, we have created leaderboards that rank the top-performing generative AI models based on various metrics such as accuracy, efficiency, and versatility. Using these leaderboards, businesses can easily identify and select the most suitable generative AI model for their needs, saving time and resources.

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50+ Models Evaluated

20 Model Ranked

3 Models Categories

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Frequently Asked Questions

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What is a Generative AI models leaderboard?

Our Generative AI models leaderboard is a ranking system that lists and compares the performance of various generative AI models based on specific evaluation metrics. The metrics can include accuracy, speed, computational efficiency, and other relevant factors. The leaderboard aims to help users identify the best-performing models for their specific application or task. The rankings are based on benchmark datasets and challenges, where multiple models are evaluated, allowing for a fair comparison of their performance. Leaderboards are useful tools for researchers, developers, and businesses seeking to identify and utilize the most effective generative AI models in their work.

What methodology is used for scoring models for the leaderboard?

We only considered prominent and open-source models to create this leaderboard. Note that this leaderboard can only be considered a high-level indicator of overall performance. The key parameters used for the scoring are;

  1. Benchmark results
  2. Model forks
  3. Model stars

Capability Rating(CR) is calculated based on the average of the selected benchmark results(BR) published in the Model's research paper.


Adoption Rating (AR) is calculated based on Model forks (MF) and Model Stars (MS). Model Stars directly indicate the community's acceptance of the Model. However, the number of stars does not necessarily mean the Model is used for project implementations. Model forks can indicate community adoption of the Model for building different applications. To calculate the adoption rating, we calculate the ratio of MS vs. MF and normalize the value to 100. 


The Model score is simply the average of scores Adoption Rating and Capability Rating.