Comparison
OpenCoder-llm vs CodeGeeX
Verdict
Pick OpenCoder-llm if openCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines; pick CodeGeeX if codeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration.
Markdown twin · OpenCoder-llm alternatives · CodeGeeX alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | OpenCoder-llm | CodeGeeX |
|---|---|---|
| Maintenance | Dormant (604d since push) As of 2w · github_public_v1 | Dormant (719d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- OpenCoder-llm
- The Open Cookbook for Top-Tier Code Large Language Models
- CodeGeeX
- CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch.
Stars
- OpenCoder-llm
- 2.1k
- CodeGeeX
- 8.8k
Forks
- OpenCoder-llm
- 125
- CodeGeeX
- 688
Open issues
- OpenCoder-llm
- 11
- CodeGeeX
- 188
Language
- OpenCoder-llm
- Python
- CodeGeeX
- Python
Adopt for
- OpenCoder-llm
- OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines.
- CodeGeeX
- CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration.
Persona
- OpenCoder-llm
- -
- CodeGeeX
- -
Runtime
- OpenCoder-llm
- -
- CodeGeeX
- -
License
- OpenCoder-llm
- MIT
- CodeGeeX
- Apache-2.0
Last pushed
- OpenCoder-llm
- Dec 8, 2024
- CodeGeeX
- Aug 13, 2024
Categories
- OpenCoder-llm
- Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training
- CodeGeeX
- LLM Frameworks, Model Training
Trust and health
Days since push
- OpenCoder-llm
- 604d
- CodeGeeX
- 719d
Open issues (now)
- OpenCoder-llm
- 11
- CodeGeeX
- 188
Owner type
- OpenCoder-llm
- User
- CodeGeeX
- Organization
OSV dependency advisories
- OpenCoder-llm
- No lockfile (source not queried)
- CodeGeeX
- Published findings
Full report
- OpenCoder-llm
- Trust report
- CodeGeeX
- Trust report
Choose OpenCoder-llm if…
- License: OpenCoder-llm is MIT, CodeGeeX is Apache-2.0.
- Tags unique to OpenCoder-llm: data filtering, dataset, evaluation-framework, large language models.
- Also covers Data & Retrieval, Evaluation & Observability.
- When you need access to both English and Chinese language support in your code generation tasks.
When NOT to use OpenCoder-llm
- If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese.
- For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process.
- If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary.
- When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks.
Choose CodeGeeX if…
- License: CodeGeeX is Apache-2.0, OpenCoder-llm is MIT.
- Tags unique to CodeGeeX: ai programming tools, pretrained-models.
- When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (OpenCoder-llm/OpenCoder-llm) · observed Aug 5, 2026
- GitHub forks (OpenCoder-llm/OpenCoder-llm) · observed Aug 5, 2026
- Last push (OpenCoder-llm/OpenCoder-llm) · observed Dec 8, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zai-org/CodeGeeX) · observed Aug 2, 2026
- GitHub forks (zai-org/CodeGeeX) · observed Aug 2, 2026
- Last push (zai-org/CodeGeeX) · observed Aug 13, 2024
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: OpenCoder-llm 2.1k · CodeGeeX 8.8k (synced Aug 5, 2026).
Common questions
- What is the difference between OpenCoder-llm and CodeGeeX?
- OpenCoder-llm: The Open Cookbook for Top-Tier Code Large Language Models. 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 OpenCoder-llm over CodeGeeX?
- Choose OpenCoder-llm over CodeGeeX when License: OpenCoder-llm is MIT, CodeGeeX is Apache-2.0; Tags unique to OpenCoder-llm: data filtering, dataset, evaluation-framework, large language models; Also covers Data & Retrieval, Evaluation & Observability; When you need access to both English and Chinese language support in your code generation tasks.
- When should I choose CodeGeeX over OpenCoder-llm?
- Choose CodeGeeX over OpenCoder-llm when License: CodeGeeX is Apache-2.0, OpenCoder-llm is MIT; Tags unique to CodeGeeX: ai programming tools, pretrained-models; When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability.
- When should I avoid OpenCoder-llm?
- If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese. For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process. If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary. When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks.
- 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 OpenCoder-llm or CodeGeeX more popular on GitHub?
- CodeGeeX has more GitHub stars (8,809 vs 2,103). Stars measure visibility, not whether either tool fits your constraints.
- Are OpenCoder-llm and CodeGeeX open source?
- Yes - both are open-source projects on GitHub (OpenCoder-llm: MIT, CodeGeeX: Apache-2.0).
- Where can I find alternatives to OpenCoder-llm or CodeGeeX?
- GraphCanon lists graph-backed alternatives at OpenCoder-llm alternatives and CodeGeeX alternatives (OpenCoder-llm markdown twin, CodeGeeX markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, OpenCoder-llm or CodeGeeX?
- OpenCoder-llm: 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 OpenCoder-llm and CodeGeeX?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OpenCoder-llm trust report; CodeGeeX trust report.