Open-source Program Synthesis Models Leaderboard

Program Synthesis Models Leaderboard

Program synthesis models are changing the game in software development!
Problem: Several program synthesis models are available in the market. But, relevant information about these models is scattered on the internet, and it is extremely difficult to evaluate these models.

Solution: We created this leaderboard to help researchers easily identify the best open-source model with an intuitive leadership quadrant graph. We evaluate the performance of open-source code models to rank them based on their capabilities and market adoption.View Models


As of March 28, the top three leaders in the open-source program synthesis models are CodeGeeX, InCoder, and CodeGen. Based on our scoring methodology, these models scored 79, 69, and 64 points, respectively. The scoring methodology is explained below. The current leader is CodeGeeX, a large-scale multilingual code generation model with 13 billion parameters pre-trained on a large code corpus of over 20 programming languages. The Model is published by Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University. As of June 22, 2022, CodeGeeX has been trained on more than 850 billion tokens on a cluster of 1,536 Ascend 910 AI Processors. CodeGeeX is available in the VS Code extension marketplace for free. It supports code completion, explanation, summarization, and more, which empowers users with a better coding experience.

RankModelSizeArchitectureOrganizationAdoption Rating
Calculated based on the number of forks and stars on the official model repo.
Capability Rating
Calculated based on the number of tasks and downstream tasks of the model.
A weighted average of the adoption and capability score of the model.
#1Codegeex13BTransformerTsinghua University926779
#2Incoder6.7BTransformerMeta AI964369
#7Polycoder2.7BTransformerCarnegie Mellon University421126

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We only considered prominent and open-source program synthesis models to create this leaderboard. Note that this leaderboard can only be considered a high-level indicator of overall performance. Depending on the specific use case and business requirements, a low-performing model in this leaderboard may be more favorable than a high-performer. 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. 


  • SantaCoder is excluded from the ranking since its number of forks data is unavailable.
  • The table and ranks will be updated as we add more open-source models in the evaluation.

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