--- title: "HLCE vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/humanity-s-last-code-exam-hlce-vs-huybery-awesome-code-llm" tools: ["humanity-s-last-code-exam-hlce", "huybery-awesome-code-llm"] --- # HLCE vs Awesome-Code-LLM *GraphCanon updated Aug 8, 2026* ## Verdict Pick HLCE if hLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes; 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. [HLCE](https://humanity-s-last-code-exam.github.io/website/) reports 96 GitHub stars, 8 forks, and 1 open issues, last pushed Aug 21, 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 [HLCE's repository](https://github.com/Humanity-s-Last-Code-Exam/HLCE) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | Source Evaluation scripts for Humanity's Last Code Exam | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 96 | 1,291 | | Forks | 8 | 74 | | Open issues | 1 | 4 | | Language | Python | - | | Adopt for | HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. | 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 | - | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 352d | 604d | | Open issues (now) | 1 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/humanity-s-last-code-exam-hlce/trust.md) | [trust report](/tools/huybery-awesome-code-llm/trust.md) | ## Decision facts: HLCE - **Adopt for:** HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. ## 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 HLCE if… - Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation. - When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively. - More recently updated (last pushed Aug 21, 2025). ### Choose Awesome-Code-LLM if… - 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, large language models. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ## When NOT to use HLCE - If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. - When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ## 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 HLCE and Awesome-Code-LLM? HLCE: Source Evaluation scripts for Humanity's Last Code Exam. 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 HLCE over Awesome-Code-LLM? Choose HLCE over Awesome-Code-LLM when Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation; When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively; More recently updated (last pushed Aug 21, 2025). ### When should I choose Awesome-Code-LLM over HLCE? Choose Awesome-Code-LLM over HLCE when 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, large language models; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I avoid HLCE? If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ### 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 HLCE or Awesome-Code-LLM more popular on GitHub? Awesome-Code-LLM has more GitHub stars (1,291 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are HLCE and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to HLCE or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [HLCE alternatives](/tools/humanity-s-last-code-exam-hlce/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([HLCE markdown twin](/tools/humanity-s-last-code-exam-hlce/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/humanity-s-last-code-exam-hlce-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, HLCE or Awesome-Code-LLM? HLCE: Slowing. 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 HLCE and Awesome-Code-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HLCE trust report](/tools/humanity-s-last-code-exam-hlce/trust); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=humanity-s-last-code-exam-hlce`](/api/graphcanon/graph?tool=humanity-s-last-code-exam-hlce) - 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/_