--- title: "Awesome-Code-LLM vs llm-leaderboard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-jonathanchaveztamales-llm-leaderboard" tools: ["huybery-awesome-code-llm", "jonathanchaveztamales-llm-leaderboard"] --- # Awesome-Code-LLM vs llm-leaderboard *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 llm-leaderboard if llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. [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. [llm-leaderboard](https://llm-stats.com) has 359 stars, 40 forks, and 14 open issues, last pushed Oct 24, 2025. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [llm-leaderboard's repository](https://github.com/JonathanChavezTamales/llm-leaderboard). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | Comprehensive LLM benchmark scores and provider prices | | Stars | 1,291 | 359 | | Forks | 74 | 40 | | Open issues | 4 | 14 | | Language | - | JavaScript | | 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. | llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | Other | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 604d | 277d | | Open issues (now) | 4 | 14 | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/jonathanchaveztamales-llm-leaderboard/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: llm-leaderboard - **Adopt for:** llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. ## Choose when ### Choose Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, llm-leaderboard is Other. - 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. ### Choose llm-leaderboard if… - License: llm-leaderboard is Other, Awesome-Code-LLM is MIT. - Tags unique to llm-leaderboard: llm, llm-agents, llm-evaluation, llmops. - When you need to compare historical performance and service costs of different LLMs within the constraints of outdated data. ## 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 llm-leaderboard - If timely or updated benchmarking data is a requirement, as llm-leaderboard's repository has been deprecated. - For real-time evaluations, as this tool does not provide current or recent performance metrics and pricing details. ## Common questions ### What is the difference between Awesome-Code-LLM and llm-leaderboard? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. llm-leaderboard: Comprehensive LLM benchmark scores and provider prices. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over llm-leaderboard? Choose Awesome-Code-LLM over llm-leaderboard when License: Awesome-Code-LLM is MIT, llm-leaderboard is Other; 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 choose llm-leaderboard over Awesome-Code-LLM? Choose llm-leaderboard over Awesome-Code-LLM when License: llm-leaderboard is Other, Awesome-Code-LLM is MIT; Tags unique to llm-leaderboard: llm, llm-agents, llm-evaluation, llmops; When you need to compare historical performance and service costs of different LLMs within the constraints of outdated data. ### 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 llm-leaderboard? If timely or updated benchmarking data is a requirement, as llm-leaderboard's repository has been deprecated. For real-time evaluations, as this tool does not provide current or recent performance metrics and pricing details. ### Is Awesome-Code-LLM or llm-leaderboard more popular on GitHub? Awesome-Code-LLM has more GitHub stars (1,291 vs 359). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and llm-leaderboard open source? Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, llm-leaderboard: Other). ### Where can I find alternatives to Awesome-Code-LLM or llm-leaderboard? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [llm-leaderboard alternatives](/tools/jonathanchaveztamales-llm-leaderboard/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [llm-leaderboard markdown twin](/tools/jonathanchaveztamales-llm-leaderboard/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-jonathanchaveztamales-llm-leaderboard.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 llm-leaderboard? Awesome-Code-LLM: Dormant. llm-leaderboard: Slowing. 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 llm-leaderboard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [llm-leaderboard trust report](/tools/jonathanchaveztamales-llm-leaderboard/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/_