--- title: "llm-leaderboard vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jonathanchaveztamales-llm-leaderboard-vs-rucaibox-llmsurvey" tools: ["jonathanchaveztamales-llm-leaderboard", "rucaibox-llmsurvey"] --- # llm-leaderboard vs LLMSurvey *GraphCanon updated Aug 17, 2026* ## Verdict Pick llm-leaderboard if llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information; 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. [llm-leaderboard](https://llm-stats.com) reports 359 GitHub stars, 40 forks, and 14 open issues, last pushed Oct 24, 2025. [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 [llm-leaderboard's repository](https://github.com/JonathanChavezTamales/llm-leaderboard) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | Comprehensive LLM benchmark scores and provider prices | A comprehensive collection of papers and resources related to Large Language Models. | | Stars | 359 | 12,205 | | Forks | 40 | 931 | | Open issues | 14 | 30 | | Language | JavaScript | Python | | Adopt for | llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. | 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 | Other | 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._ | | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 277d | 523d | | Open issues (now) | 14 | 30 | | Stars delta | Unknown | +18 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/jonathanchaveztamales-llm-leaderboard/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: llm-leaderboard - **Adopt for:** llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. ## 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 llm-leaderboard if… - llm-leaderboard is primarily JavaScript; LLMSurvey is Python. - Tags unique to llm-leaderboard: llm-agents, llm-evaluation, llmops, llms-benchmarking. - When you need to compare historical performance and service costs of different LLMs within the constraints of outdated data. ### Choose LLMSurvey if… - LLMSurvey is primarily Python; llm-leaderboard is JavaScript. - 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, large language models. - 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 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. ## 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 llm-leaderboard and LLMSurvey? llm-leaderboard: Comprehensive LLM benchmark scores and provider prices. 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 llm-leaderboard over LLMSurvey? Choose llm-leaderboard over LLMSurvey when llm-leaderboard is primarily JavaScript; LLMSurvey is Python; Tags unique to llm-leaderboard: llm-agents, llm-evaluation, llmops, llms-benchmarking; When you need to compare historical performance and service costs of different LLMs within the constraints of outdated data. ### When should I choose LLMSurvey over llm-leaderboard? Choose LLMSurvey over llm-leaderboard when LLMSurvey is primarily Python; llm-leaderboard is JavaScript; 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, large language models; 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 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. ### 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 llm-leaderboard or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,205 vs 359). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-leaderboard and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-leaderboard or LLMSurvey? GraphCanon lists graph-backed alternatives at [llm-leaderboard alternatives](/tools/jonathanchaveztamales-llm-leaderboard/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([llm-leaderboard markdown twin](/tools/jonathanchaveztamales-llm-leaderboard/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/jonathanchaveztamales-llm-leaderboard-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-leaderboard or LLMSurvey? llm-leaderboard: Slowing. 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 llm-leaderboard and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-leaderboard trust report](/tools/jonathanchaveztamales-llm-leaderboard/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jonathanchaveztamales-llm-leaderboard`](/api/graphcanon/graph?tool=jonathanchaveztamales-llm-leaderboard) - 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/_