--- title: "open-llms vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-open-llms-vs-huybery-awesome-code-llm" tools: ["eugeneyan-open-llms", "huybery-awesome-code-llm"] --- # open-llms vs Awesome-Code-LLM *GraphCanon updated Aug 17, 2026* ## Verdict Pick open-llms if critical Facts for 'open-llms' Tool Usage; pick Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. [open-llms](https://github.com/eugeneyan/open-llms) reports 13k GitHub stars, 985 forks, and 11 open issues, last pushed Feb 13, 2025. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) has 1.3k stars, 74 forks, and 4 open issues, last pushed Dec 10, 2024. Figures are from public GitHub metadata via [open-llms's repository](https://github.com/eugeneyan/open-llms) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [open-llms](/tools/eugeneyan-open-llms.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | A list of open LLMs available for commercial use. | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 12,849 | 1,291 | | Forks | 985 | 74 | | Open issues | 11 | 4 | | Language | - | - | | Adopt for | Critical Facts for 'open-llms' Tool Usage | Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. | | Persona | - | - | | Runtime | - | - | | License | The repository itself is licensed under the permissive Apache-2.0 license; however, it aggregates data about various LLMs that may have differing licensing conditions including but not limited to the | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | | Categories | LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-llms](/tools/eugeneyan-open-llms.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Days since push | 549d | 604d | | Open issues (now) | 11 | 4 | | Stars delta | +18 (30d) | Unknown | | Open issues delta | -2 (30d) | Unknown | | Full report | [trust report](/tools/eugeneyan-open-llms/trust.md) | [trust report](/tools/huybery-awesome-code-llm/trust.md) | ## Decision facts: open-llms - **Pricing:** freemium - Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements. - **Adopt for:** Critical Facts for 'open-llms' Tool Usage - **License detail:** The repository itself is licensed under the permissive Apache-2.0 license; however, it aggregates data about various LLMs that may have differing licensing conditions including but not limited to the ## Decision facts: Awesome-Code-LLM - **Requirements:** No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs. - **Adopt for:** Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. - **License detail:** MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. ## Choose when ### Choose open-llms if… - License: open-llms is Apache-2.0, Awesome-Code-LLM is MIT. - Pricing: Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements.. - Tags unique to open-llms: commercial, llm, llms. - When you need a curated list of open-source large language models (LLMs) that are specifically licensed for commercial use. ### Choose Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, open-llms is Apache-2.0. - Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.. - Tags unique to Awesome-Code-LLM: awesome, code generation. - Also covers Evaluation & Observability. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ## When NOT to use open-llms - If you require proprietary or closed-source LLMs as this repository exclusively lists open-source models that are available for commercial use under permissive licenses such as Apache 2.0. - For projects needing a detailed technical implementation guide of each listed LLM, since 'open-llms' acts primarily as an index rather than providing in-depth tutorials on implementing and training L4 ## When NOT to use Awesome-Code-LLM - When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. - If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. - In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ## Common questions ### What is the difference between open-llms and Awesome-Code-LLM? open-llms: A list of open LLMs available for commercial use.. Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose open-llms over Awesome-Code-LLM? Choose open-llms over Awesome-Code-LLM when License: open-llms is Apache-2.0, Awesome-Code-LLM is MIT; Pricing: Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements.; Tags unique to open-llms: commercial, llm, llms; When you need a curated list of open-source large language models (LLMs) that are specifically licensed for commercial use. ### When should I choose Awesome-Code-LLM over open-llms? Choose Awesome-Code-LLM over open-llms when License: Awesome-Code-LLM is MIT, open-llms is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, code generation; Also covers Evaluation & Observability; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I avoid open-llms? If you require proprietary or closed-source LLMs as this repository exclusively lists open-source models that are available for commercial use under permissive licenses such as Apache 2.0. For projects needing a detailed technical implementation guide of each listed LLM, since 'open-llms' acts primarily as an index rather than providing in-depth tutorials on implementing and training L4 ### When should I avoid Awesome-Code-LLM? When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ### Is open-llms or Awesome-Code-LLM more popular on GitHub? open-llms has more GitHub stars (12,849 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are open-llms and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub (open-llms: Apache-2.0, Awesome-Code-LLM: MIT). ### Where can I find alternatives to open-llms or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [open-llms alternatives](/tools/eugeneyan-open-llms/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([open-llms markdown twin](/tools/eugeneyan-open-llms/alternatives.md), [Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/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/eugeneyan-open-llms-vs-huybery-awesome-code-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, open-llms or Awesome-Code-LLM? open-llms: Dormant. Awesome-Code-LLM: 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 open-llms and Awesome-Code-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-llms trust report](/tools/eugeneyan-open-llms/trust); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-open-llms`](/api/graphcanon/graph?tool=eugeneyan-open-llms) - 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/_