--- title: "Awesome-Code-LLM vs LiveCodeBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-livecodebench-livecodebench" tools: ["huybery-awesome-code-llm", "livecodebench-livecodebench"] --- # Awesome-Code-LLM vs LiveCodeBench *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 LiveCodeBench if liveCodeBench offers an in-depth approach to evaluating large language models specifically for code tasks such as generation and repair. [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. [LiveCodeBench](https://livecodebench.github.io/) has 925 stars, 195 forks, and 38 open issues, last pushed Jul 16, 2025. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [LiveCodeBench's repository](https://github.com/LiveCodeBench/LiveCodeBench). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [LiveCodeBench](/tools/livecodebench-livecodebench.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | Holistic and contamination-free evaluation of large language models for code | | Stars | 1,291 | 925 | | Forks | 74 | 195 | | Open issues | 4 | 38 | | 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. | LiveCodeBench offers an in-depth approach to evaluating large language models specifically for code tasks such as generation and repair. | | 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) | [LiveCodeBench](/tools/livecodebench-livecodebench.md) | | --- | --- | --- | | Days since push | 604d | 385d | | Open issues (now) | 4 | 38 | | Owner type | User | Organization | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/livecodebench-livecodebench/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: LiveCodeBench - **Adopt for:** LiveCodeBench offers an in-depth approach to evaluating large language models specifically for code tasks such as generation and repair. ## 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, large language models. - 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 LiveCodeBench if… - Tags unique to LiveCodeBench: code-execution, code-repair, gpt-4, python. - When you need a holistic method to assess the effectiveness of LLMs in code tasks without risking contamination by earlier outputs or data leakage. - More recently updated (last pushed Jul 16, 2025). ## 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 LiveCodeBench - For broad, non-code-specific model assessments where a more generalized evaluation tool would suffice. - If your project is not compatible with Python 3.11 or if you do not want to use the uv dependency manager recommended by LiveCodeBench. ## Common questions ### What is the difference between Awesome-Code-LLM and LiveCodeBench? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. LiveCodeBench: Holistic and contamination-free evaluation of large language models for code. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over LiveCodeBench? Choose Awesome-Code-LLM over LiveCodeBench 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, large language models; 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 LiveCodeBench over Awesome-Code-LLM? Choose LiveCodeBench over Awesome-Code-LLM when Tags unique to LiveCodeBench: code-execution, code-repair, gpt-4, python; When you need a holistic method to assess the effectiveness of LLMs in code tasks without risking contamination by earlier outputs or data leakage; More recently updated (last pushed Jul 16, 2025). ### 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 LiveCodeBench? For broad, non-code-specific model assessments where a more generalized evaluation tool would suffice. If your project is not compatible with Python 3.11 or if you do not want to use the uv dependency manager recommended by LiveCodeBench. ### Is Awesome-Code-LLM or LiveCodeBench more popular on GitHub? Awesome-Code-LLM has more GitHub stars (1,291 vs 925). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and LiveCodeBench open source? Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, LiveCodeBench: MIT). ### Where can I find alternatives to Awesome-Code-LLM or LiveCodeBench? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [LiveCodeBench alternatives](/tools/livecodebench-livecodebench/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [LiveCodeBench markdown twin](/tools/livecodebench-livecodebench/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-livecodebench-livecodebench.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 LiveCodeBench? Awesome-Code-LLM: Dormant. LiveCodeBench: 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 LiveCodeBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [LiveCodeBench trust report](/tools/livecodebench-livecodebench/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/_