--- title: "CodeBERT vs CodeRL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-codebert-vs-salesforce-coderl" tools: ["microsoft-codebert", "salesforce-coderl"] --- # CodeBERT vs CodeRL *GraphCanon updated Aug 5, 2026* ## Verdict Pick CodeBERT if codeBERT is an advanced pre-trained model for programming and natural language tasks in multiple languages like Python and Java; pick CodeRL if codeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. [CodeBERT](https://github.com/microsoft/CodeBERT) reports 2.8k GitHub stars, 497 forks, and 86 open issues, last pushed Jul 9, 2023. [CodeRL](https://github.com/salesforce/CodeRL) has 574 stars, 69 forks, and 42 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [CodeBERT's repository](https://github.com/microsoft/CodeBERT) and [CodeRL's repository](https://github.com/salesforce/CodeRL). | | [CodeBERT](/tools/microsoft-codebert.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Tagline | CodeBERT series models for code pretraining in Python and programming languages | CodeRL: Combines pretrained models and reinforcement learning for code generation. | | Stars | 2,787 | 574 | | Forks | 497 | 69 | | Open issues | 86 | 42 | | Language | Python | Python | | Adopt for | CodeBERT is an advanced pre-trained model for programming and natural language tasks in multiple languages like Python and Java. | CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. | | Persona | - | - | | Runtime | - | - | | License | MIT | BSD-3-Clause | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [CodeBERT](/tools/microsoft-codebert.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1123d | 63d | | Open issues (now) | 86 | 42 | | Full report | [trust report](/tools/microsoft-codebert/trust.md) | [trust report](/tools/salesforce-coderl/trust.md) | ## Shared compatibility - **Python**: [CodeBERT](/tools/microsoft-codebert.md) - Python runtime; [CodeRL](/tools/salesforce-coderl.md) - Python runtime ## Decision facts: CodeBERT - **Requirements:** Install torch and transformers via pip before using CodeBERT for embedding generation or other tasks; Ensure Python and Hugging Face's transformers framework are available, as they form the core execution environment for utilizing this model - **Adopt for:** CodeBERT is an advanced pre-trained model for programming and natural language tasks in multiple languages like Python and Java. ## Decision facts: CodeRL - **Adopt for:** CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code. ## Choose when ### Choose CodeBERT if… - License: CodeBERT is MIT, CodeRL is BSD-3-Clause. - Requirements: Install torch and transformers via pip before using CodeBERT for embedding generation or other tasks; Ensure Python and Hugging Face's transformers framework are available, as they form the core execution environment for utilizing this model. - Tags unique to CodeBERT: code pretraining, transformers framework. - When you need to work on tasks involving both programming and natural language processing across six different programming languages: Python, Java, JavaScript, PHP, Ruby, Go ### Choose CodeRL if… - License: CodeRL is BSD-3-Clause, CodeBERT is MIT. - Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning. - Also covers Developer Tools. - When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques. ## When NOT to use CodeBERT - Avoid for direct mask prediction tasks without MLM (Masked Language Model) fine-tuning as CodeBERT is not natively equipped for such tasks unlike its variant designed with MLM capabilities - Do not consider it if your project requires pre-training models on a wider variety of programming languages beyond the six supported by this model ## When NOT to use CodeRL - Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. - Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues. ## Common questions ### What is the difference between CodeBERT and CodeRL? CodeBERT: CodeBERT series models for code pretraining in Python and programming languages. CodeRL: CodeRL: Combines pretrained models and reinforcement learning for code generation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose CodeBERT over CodeRL? Choose CodeBERT over CodeRL when License: CodeBERT is MIT, CodeRL is BSD-3-Clause; Requirements: Install torch and transformers via pip before using CodeBERT for embedding generation or other tasks; Ensure Python and Hugging Face's transformers framework are available, as they form the core execution environment for utilizing this model; Tags unique to CodeBERT: code pretraining, transformers framework; When you need to work on tasks involving both programming and natural language processing across six different programming languages: Python, Java, JavaScript, PHP, Ruby, Go. ### When should I choose CodeRL over CodeBERT? Choose CodeRL over CodeBERT when License: CodeRL is BSD-3-Clause, CodeBERT is MIT; Tags unique to CodeRL: ai, codegeneration, languagemodel, machinelearning; Also covers Developer Tools; When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques. ### When should I avoid CodeBERT? Avoid for direct mask prediction tasks without MLM (Masked Language Model) fine-tuning as CodeBERT is not natively equipped for such tasks unlike its variant designed with MLM capabilities Do not consider it if your project requires pre-training models on a wider variety of programming languages beyond the six supported by this model ### When should I avoid CodeRL? Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues. ### Is CodeBERT or CodeRL more popular on GitHub? CodeBERT has more GitHub stars (2,787 vs 574). Stars measure visibility, not whether either tool fits your constraints. ### Are CodeBERT and CodeRL open source? Yes - both are open-source projects on GitHub (CodeBERT: MIT, CodeRL: BSD-3-Clause). ### Where can I find alternatives to CodeBERT or CodeRL? GraphCanon lists graph-backed alternatives at [CodeBERT alternatives](/tools/microsoft-codebert/alternatives) and [CodeRL alternatives](/tools/salesforce-coderl/alternatives) ([CodeBERT markdown twin](/tools/microsoft-codebert/alternatives.md), [CodeRL markdown twin](/tools/salesforce-coderl/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/microsoft-codebert-vs-salesforce-coderl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, CodeBERT or CodeRL? CodeBERT: Dormant. CodeRL: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for CodeBERT and CodeRL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [CodeBERT trust report](/tools/microsoft-codebert/trust); [CodeRL trust report](/tools/salesforce-coderl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-codebert`](/api/graphcanon/graph?tool=microsoft-codebert) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_