--- title: "MultiPL-E vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nuprl-multipl-e-vs-rucaibox-llmsurvey" tools: ["nuprl-multipl-e", "rucaibox-llmsurvey"] --- # MultiPL-E vs LLMSurvey *GraphCanon updated Aug 17, 2026* ## Verdict Pick MultiPL-E if multiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages; 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. [MultiPL-E](https://github.com/nuprl/MultiPL-E) reports 313 GitHub stars, 57 forks, and 16 open issues, last pushed Apr 12, 2026. [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 [MultiPL-E's repository](https://github.com/nuprl/MultiPL-E) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [MultiPL-E](/tools/nuprl-multipl-e.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | A multi-programming language benchmark for LLMs | A comprehensive collection of papers and resources related to Large Language Models. | | Stars | 313 | 12,205 | | Forks | 57 | 931 | | Open issues | 16 | 30 | | Language | Python | Python | | Adopt for | MultiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages. | 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._ | | [MultiPL-E](/tools/nuprl-multipl-e.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 115d | 523d | | Open issues (now) | 16 | 30 | | Stars delta | Unknown | +18 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/nuprl-multipl-e/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: MultiPL-E - **Pricing:** freemium - Free to use but requires local compute resources and potentially licensed libraries - **Adopt for:** MultiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages. - **License detail:** Other ## 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 MultiPL-E if… - Pricing: Free to use but requires local compute resources and potentially licensed libraries. - Tags unique to MultiPL-E: ai benchmark, benchmarking, code generation, multilingual benchmark. - Use MultiPL-E for evaluating large language models' performance on code generation tasks in different languages directly without needing to create new benchmarks from scratch. ### 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, 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 MultiPL-E - Avoid using MultiPL-E if you need a more challenging benchmark; consider Ag-LiveCodeBench-X instead. - Do not use MultiPL-E if your evaluation environment lacks GPU resources for completion generation or does not support Docker or Podman for execution of generated code. ## 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 MultiPL-E and LLMSurvey? MultiPL-E: A multi-programming language benchmark for LLMs. 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 MultiPL-E over LLMSurvey? Choose MultiPL-E over LLMSurvey when Pricing: Free to use but requires local compute resources and potentially licensed libraries; Tags unique to MultiPL-E: ai benchmark, benchmarking, code generation, multilingual benchmark; Use MultiPL-E for evaluating large language models' performance on code generation tasks in different languages directly without needing to create new benchmarks from scratch. ### When should I choose LLMSurvey over MultiPL-E? Choose LLMSurvey over MultiPL-E 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, 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 MultiPL-E? Avoid using MultiPL-E if you need a more challenging benchmark; consider Ag-LiveCodeBench-X instead. Do not use MultiPL-E if your evaluation environment lacks GPU resources for completion generation or does not support Docker or Podman for execution of generated code. ### 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 MultiPL-E or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,205 vs 313). Stars measure visibility, not whether either tool fits your constraints. ### Are MultiPL-E and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to MultiPL-E or LLMSurvey? GraphCanon lists graph-backed alternatives at [MultiPL-E alternatives](/tools/nuprl-multipl-e/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([MultiPL-E markdown twin](/tools/nuprl-multipl-e/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/nuprl-multipl-e-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MultiPL-E or LLMSurvey? MultiPL-E: 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 MultiPL-E and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MultiPL-E trust report](/tools/nuprl-multipl-e/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nuprl-multipl-e`](/api/graphcanon/graph?tool=nuprl-multipl-e) - 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/_