--- title: "Awesome-Code-LLM vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-rucaibox-llmsurvey" tools: ["huybery-awesome-code-llm", "rucaibox-llmsurvey"] --- # Awesome-Code-LLM vs LLMSurvey *GraphCanon updated Aug 17, 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 LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训. [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. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 931 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | A comprehensive collection of papers and resources related to Large Language Models. | | Stars | 1,291 | 12,205 | | Forks | 74 | 931 | | Open issues | 4 | 30 | | 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. | LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | The license for LLMSurvey is unknown based on the provided repository information. | | 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) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Days since push | 604d | 523d | | Open issues (now) | 4 | 30 | | Stars delta | Unknown | +18 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/rucaibox-llmsurvey/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: LLMSurvey - **Pricing:** freemium - Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage - **Adopt for:** LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 - **License detail:** The license for LLMSurvey is unknown based on the provided repository information. ## 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. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### Choose LLMSurvey if… - Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage. - Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, llm. - You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series 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 LLMSurvey - You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. - Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how ## Common questions ### What is the difference between Awesome-Code-LLM and LLMSurvey? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over LLMSurvey? Choose Awesome-Code-LLM over LLMSurvey 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; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I choose LLMSurvey over Awesome-Code-LLM? Choose LLMSurvey over Awesome-Code-LLM when Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, llm; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series 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 LLMSurvey? You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how ### Is Awesome-Code-LLM or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,205 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Code-LLM or LLMSurvey? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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-rucaibox-llmsurvey.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 LLMSurvey? Awesome-Code-LLM: Dormant. LLMSurvey: 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 LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/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/_