--- title: "chain-of-thought-hub vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/franxyao-chain-of-thought-hub-vs-rucaibox-llmsurvey" tools: ["franxyao-chain-of-thought-hub", "rucaibox-llmsurvey"] --- # chain-of-thought-hub vs LLMSurvey *GraphCanon updated Jul 11, 2026* ## Verdict Pick chain-of-thought-hub if chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM; 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. [chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub) reports 2.8k GitHub stars, 144 forks, and 27 open issues, last pushed Aug 4, 2024. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 935 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [chain-of-thought-hub's repository](https://github.com/FranxYao/chain-of-thought-hub) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | A comprehensive collection of papers and resources related to Large Language Models. | | Stars | 2,777 | 12,187 | | Forks | 144 | 935 | | Open issues | 27 | 30 | | Language | Jupyter Notebook | Python | | Adopt for | Chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM | 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 | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | The license for LLMSurvey is unknown based on the provided repository information. | | Categories | Evaluation & Observability | LLM Frameworks, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Days since push | 706d | 487d | | Open issues (now) | 27 | 30 | | Owner type | User | Organization | | Full report | [trust report](/tools/franxyao-chain-of-thought-hub/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: chain-of-thought-hub - **Requirements:** Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks - **Adopt for:** Chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM - **License detail:** The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. ## 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 chain-of-thought-hub if… - chain-of-thought-hub is primarily Jupyter Notebook; LLMSurvey is Python. - Requirements: Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks. - Tags unique to chain-of-thought-hub: complex reasoning, chain-of-thought prompting, llm-benchmarking. - Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ### Choose LLMSurvey if… - LLMSurvey is primarily Python; chain-of-thought-hub is Jupyter Notebook. - 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: pre-training, chain-of-thought, llm, instruction-tuning. - Also covers LLM Frameworks. - 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 chain-of-thought-hub - Do not use Chain-of-Thought Hub if your focus is on general conversational capabilities rather than specific, challenging problem-solving tasks. - Avoid this tool if you are primarily interested in simpler language processing tasks that do not involve chain-of-thought prompting or complex datasets. ## 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 chain-of-thought-hub and LLMSurvey? chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting. 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 chain-of-thought-hub over LLMSurvey? Choose chain-of-thought-hub over LLMSurvey when chain-of-thought-hub is primarily Jupyter Notebook; LLMSurvey is Python; Requirements: Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks; Tags unique to chain-of-thought-hub: complex reasoning, chain-of-thought prompting, llm-benchmarking; Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ### When should I choose LLMSurvey over chain-of-thought-hub? Choose LLMSurvey over chain-of-thought-hub when LLMSurvey is primarily Python; chain-of-thought-hub is Jupyter Notebook; 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: pre-training, chain-of-thought, llm, instruction-tuning; Also covers LLM Frameworks; 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 chain-of-thought-hub? Do not use Chain-of-Thought Hub if your focus is on general conversational capabilities rather than specific, challenging problem-solving tasks. Avoid this tool if you are primarily interested in simpler language processing tasks that do not involve chain-of-thought prompting or complex datasets. ### 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 chain-of-thought-hub or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,187 vs 2,777). Stars measure visibility, not whether either tool fits your constraints. ### Are chain-of-thought-hub and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to chain-of-thought-hub or LLMSurvey? GraphCanon lists graph-backed alternatives at [chain-of-thought-hub alternatives](/tools/franxyao-chain-of-thought-hub/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([chain-of-thought-hub markdown twin](/tools/franxyao-chain-of-thought-hub/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/franxyao-chain-of-thought-hub-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, chain-of-thought-hub or LLMSurvey? chain-of-thought-hub: 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 chain-of-thought-hub and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chain-of-thought-hub trust report](/tools/franxyao-chain-of-thought-hub/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub`](/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub) - 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/_