--- title: "Awesome-Code-LLM vs human-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-openai-human-eval" tools: ["huybery-awesome-code-llm", "openai-human-eval"] --- # Awesome-Code-LLM vs human-eval *GraphCanon updated Aug 6, 2026* ## Verdict 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; pick human-eval if human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) reports 1.3k GitHub stars, 74 forks, and 4 open issues, last pushed Dec 10, 2024. [human-eval](https://github.com/openai/human-eval) has 3.3k stars, 452 forks, and 44 open issues, last pushed Jan 17, 2025. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [human-eval's repository](https://github.com/openai/human-eval). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [human-eval](/tools/openai-human-eval.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | Evaluating Large Language Models Trained on Code | | Stars | 1,291 | 3,331 | | Forks | 74 | 452 | | Open issues | 4 | 44 | | Language | - | Python | | 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. | human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | MIT | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [human-eval](/tools/openai-human-eval.md) | | --- | --- | --- | | Days since push | 604d | 564d | | Open issues (now) | 4 | 44 | | Owner type | User | Organization | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/openai-human-eval/trust.md) | ## 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. ## Decision facts: human-eval - **Hosting:** self hosted - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process. - **Pricing:** freemium - The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs. - **Adopt for:** human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. ## Choose when ### 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. - Also covers LLM Frameworks. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### Choose human-eval if… - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process. - Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.. - Tags unique to human-eval: code evaluation, python. - When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models. ## 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 ## When NOT to use human-eval - If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems. - When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies. ## Common questions ### What is the difference between Awesome-Code-LLM and human-eval? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. human-eval: Evaluating Large Language Models Trained on Code. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over human-eval? Choose Awesome-Code-LLM over human-eval 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; Also covers LLM Frameworks; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I choose human-eval over Awesome-Code-LLM? Choose human-eval over Awesome-Code-LLM when This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process; Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.; Tags unique to human-eval: code evaluation, python; When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models. ### 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 ### When should I avoid human-eval? If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems. When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies. ### Is Awesome-Code-LLM or human-eval more popular on GitHub? human-eval has more GitHub stars (3,331 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and human-eval open source? Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, human-eval: MIT). ### Where can I find alternatives to Awesome-Code-LLM or human-eval? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [human-eval alternatives](/tools/openai-human-eval/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [human-eval markdown twin](/tools/openai-human-eval/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/huybery-awesome-code-llm-vs-openai-human-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Code-LLM or human-eval? Awesome-Code-LLM: Dormant. human-eval: 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 Awesome-Code-LLM and human-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [human-eval trust report](/tools/openai-human-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huybery-awesome-code-llm`](/api/graphcanon/graph?tool=huybery-awesome-code-llm) - 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/_