--- title: "lever vs CodeGeeX" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/niansong1996-lever-vs-zai-org-codegeex" tools: ["niansong1996-lever", "zai-org-codegeex"] --- # lever vs CodeGeeX *GraphCanon updated Aug 5, 2026* ## Verdict Pick lever if lever offers support for verifying language-to-code generation through actual code execution; pick CodeGeeX if codeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration. [lever](https://arxiv.org/abs/2302.08468) reports 90 GitHub stars, 8 forks, and 2 open issues, last pushed Jul 5, 2023. [CodeGeeX](https://codegeex.cn) has 8.8k stars, 688 forks, and 188 open issues, last pushed Aug 13, 2024. Figures are from public GitHub metadata via [lever's repository](https://github.com/niansong1996/lever) and [CodeGeeX's repository](https://github.com/zai-org/CodeGeeX). | | [lever](/tools/niansong1996-lever.md) | [CodeGeeX](/tools/zai-org-codegeex.md) | | --- | --- | --- | | Tagline | Supports learning to verify language-to-code generation with execution | CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch. | | Stars | 90 | 8,809 | | Forks | 8 | 688 | | Open issues | 2 | 188 | | Language | Python | Python | | Adopt for | Lever offers support for verifying language-to-code generation through actual code execution. | CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration. | | Persona | - | - | | Runtime | - | - | | License | Lever's source code is freely available under an MIT License for modification and distribution in both personal and commercial projects. | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lever](/tools/niansong1996-lever.md) | [CodeGeeX](/tools/zai-org-codegeex.md) | | --- | --- | --- | | Days since push | 1127d | 719d | | Open issues (now) | 2 | 188 | | Owner type | User | Organization | | Full report | [trust report](/tools/niansong1996-lever/trust.md) | [trust report](/tools/zai-org-codegeex/trust.md) | ## Shared compatibility - **Python**: [lever](/tools/niansong1996-lever.md) - Python runtime; [CodeGeeX](/tools/zai-org-codegeex.md) - Python runtime ## 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. ## Decision facts: CodeGeeX - **Adopt for:** CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration. ## Choose when ### Choose lever if… - License: lever is MIT, CodeGeeX is Apache-2.0. - 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. ### Choose CodeGeeX if… - License: CodeGeeX is Apache-2.0, lever is MIT. - Tags unique to CodeGeeX: ai programming tools, code generation, pretrained-models. - Also covers LLM Frameworks. - When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability. ## 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. ## When NOT to use CodeGeeX - If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+. - In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient. ## Common questions ### What is the difference between lever and CodeGeeX? lever: Supports learning to verify language-to-code generation with execution. CodeGeeX: CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose lever over CodeGeeX? Choose lever over CodeGeeX when License: lever is MIT, CodeGeeX is Apache-2.0; 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 choose CodeGeeX over lever? Choose CodeGeeX over lever when License: CodeGeeX is Apache-2.0, lever is MIT; Tags unique to CodeGeeX: ai programming tools, code generation, pretrained-models; Also covers LLM Frameworks; When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability. ### 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. ### When should I avoid CodeGeeX? If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+. In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient. ### Is lever or CodeGeeX more popular on GitHub? CodeGeeX has more GitHub stars (8,809 vs 90). Stars measure visibility, not whether either tool fits your constraints. ### Are lever and CodeGeeX open source? Yes - both are open-source projects on GitHub (lever: MIT, CodeGeeX: Apache-2.0). ### Where can I find alternatives to lever or CodeGeeX? GraphCanon lists graph-backed alternatives at [lever alternatives](/tools/niansong1996-lever/alternatives) and [CodeGeeX alternatives](/tools/zai-org-codegeex/alternatives) ([lever markdown twin](/tools/niansong1996-lever/alternatives.md), [CodeGeeX markdown twin](/tools/zai-org-codegeex/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/niansong1996-lever-vs-zai-org-codegeex.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lever or CodeGeeX? lever: Dormant. CodeGeeX: 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 lever and CodeGeeX? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lever trust report](/tools/niansong1996-lever/trust); [CodeGeeX trust report](/tools/zai-org-codegeex/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=niansong1996-lever`](/api/graphcanon/graph?tool=niansong1996-lever) - 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/_