--- title: "CodeBERT vs lever" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-codebert-vs-niansong1996-lever" tools: ["microsoft-codebert", "niansong1996-lever"] --- # CodeBERT vs lever *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 lever if lever offers support for verifying language-to-code generation through actual code execution. [CodeBERT](https://github.com/microsoft/CodeBERT) reports 2.8k GitHub stars, 497 forks, and 86 open issues, last pushed Jul 9, 2023. [lever](https://arxiv.org/abs/2302.08468) has 90 stars, 8 forks, and 2 open issues, last pushed Jul 5, 2023. Figures are from public GitHub metadata via [CodeBERT's repository](https://github.com/microsoft/CodeBERT) and [lever's repository](https://github.com/niansong1996/lever). | | [CodeBERT](/tools/microsoft-codebert.md) | [lever](/tools/niansong1996-lever.md) | | --- | --- | --- | | Tagline | CodeBERT series models for code pretraining in Python and programming languages | Supports learning to verify language-to-code generation with execution | | Stars | 2,787 | 90 | | Forks | 497 | 8 | | Open issues | 86 | 2 | | 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. | Lever offers support for verifying language-to-code generation through actual code execution. | | Persona | - | - | | Runtime | - | - | | License | MIT | Lever's source code is freely available under an MIT License for modification and distribution in both personal and commercial projects. | | Categories | Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [CodeBERT](/tools/microsoft-codebert.md) | [lever](/tools/niansong1996-lever.md) | | --- | --- | --- | | Days since push | 1123d | 1127d | | Open issues (now) | 86 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/microsoft-codebert/trust.md) | [trust report](/tools/niansong1996-lever/trust.md) | ## Shared compatibility - **Python**: [CodeBERT](/tools/microsoft-codebert.md) - Python runtime; [lever](/tools/niansong1996-lever.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: lever - **Requirements:** All of the pipelines have been tested on Linux machines only, requiring possibly custom `tree-sitter` parsers for other platforms. - **Adopt for:** Lever offers support for verifying language-to-code generation through actual code execution. - **License detail:** Lever's source code is freely available under an MIT License for modification and distribution in both personal and commercial projects. ## Choose when ### Choose CodeBERT if… - 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 lever if… - Requirements: All of the pipelines have been tested on Linux machines only, requiring possibly custom `tree-sitter` parsers for other platforms.. - Tags unique to lever: code verification, execution based verification, language-to-code. - Also covers Evaluation & Observability. - When the development team needs to verify accuracy of generated code from language inputs based on execution, and has access to Linux machines to ensure seamless functionality. ## 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 lever - Avoid Lever if developing in an environment other than Linux as it requires the use of tree-sitter parsers, which may not be compatible with your system. - Do not choose Lever if you seek a tool that does not require setting up a conda environment and installing specific dependencies to operate. ## Common questions ### What is the difference between CodeBERT and lever? CodeBERT: CodeBERT series models for code pretraining in Python and programming languages. lever: Supports learning to verify language-to-code generation with execution. See the comparison table for live GitHub stats and shared categories. ### When should I choose CodeBERT over lever? Choose CodeBERT over lever when 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 lever over CodeBERT? Choose lever over CodeBERT when Requirements: All of the pipelines have been tested on Linux machines only, requiring possibly custom `tree-sitter` parsers for other platforms.; Tags unique to lever: code verification, execution based verification, language-to-code; Also covers Evaluation & Observability; When the development team needs to verify accuracy of generated code from language inputs based on execution, and has access to Linux machines to ensure seamless functionality. ### 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 lever? Avoid Lever if developing in an environment other than Linux as it requires the use of tree-sitter parsers, which may not be compatible with your system. Do not choose Lever if you seek a tool that does not require setting up a conda environment and installing specific dependencies to operate. ### Is CodeBERT or lever more popular on GitHub? CodeBERT has more GitHub stars (2,787 vs 90). Stars measure visibility, not whether either tool fits your constraints. ### Are CodeBERT and lever open source? Yes - both are open-source projects on GitHub (CodeBERT: MIT, lever: MIT). ### Where can I find alternatives to CodeBERT or lever? GraphCanon lists graph-backed alternatives at [CodeBERT alternatives](/tools/microsoft-codebert/alternatives) and [lever alternatives](/tools/niansong1996-lever/alternatives) ([CodeBERT markdown twin](/tools/microsoft-codebert/alternatives.md), [lever markdown twin](/tools/niansong1996-lever/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-niansong1996-lever.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, CodeBERT or lever? CodeBERT: Dormant. lever: Dormant. 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 lever? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [CodeBERT trust report](/tools/microsoft-codebert/trust); [lever trust report](/tools/niansong1996-lever/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/_