--- title: "mteb vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/embeddings-benchmark-mteb-vs-rucaibox-llmsurvey" tools: ["embeddings-benchmark-mteb", "rucaibox-llmsurvey"] --- # mteb vs LLMSurvey *GraphCanon updated Jul 12, 2026* ## Verdict Pick mteb when tags unique to mteb: benchmark, embeddings, evaluation, information-retrieval; pick LLMSurvey 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. [mteb](https://docs.mteb.org) reports 3.3k GitHub stars, 638 forks, and 295 open issues, last pushed Jul 9, 2026. [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 [mteb's repository](https://github.com/embeddings-benchmark/mteb) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [mteb](/tools/embeddings-benchmark-mteb.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | State-of-the-art evaluation of embeddings across languages and modalities | A comprehensive collection of papers and resources related to Large Language Models. | | Stars | 3,349 | 12,187 | | Forks | 638 | 935 | | Open issues | 295 | 30 | | Language | Python | Python | | 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 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The license for LLMSurvey is unknown based on the provided repository information. | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mteb](/tools/embeddings-benchmark-mteb.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 487d | | Open issues (now) | 295 | 30 | | Full report | [trust report](/tools/embeddings-benchmark-mteb/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## 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 mteb if… - Tags unique to mteb: benchmark, embeddings, evaluation, information-retrieval. - mteb ships Docker support for self-hosted deployment. - More recently updated (last pushed Jul 9, 2026). ### 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. - 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 mteb - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 mteb and LLMSurvey? mteb: State-of-the-art evaluation of embeddings across languages and modalities. 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 mteb over LLMSurvey? Choose mteb over LLMSurvey when Tags unique to mteb: benchmark, embeddings, evaluation, information-retrieval; mteb ships Docker support for self-hosted deployment; More recently updated (last pushed Jul 9, 2026). ### When should I choose LLMSurvey over mteb? Choose LLMSurvey over mteb 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; 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 mteb? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 mteb or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,187 vs 3,349). Stars measure visibility, not whether either tool fits your constraints. ### Are mteb and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to mteb or LLMSurvey? GraphCanon lists graph-backed alternatives at [mteb alternatives](/tools/embeddings-benchmark-mteb/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([mteb markdown twin](/tools/embeddings-benchmark-mteb/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/embeddings-benchmark-mteb-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mteb or LLMSurvey? mteb: Very active. 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 mteb and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mteb trust report](/tools/embeddings-benchmark-mteb/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=embeddings-benchmark-mteb`](/api/graphcanon/graph?tool=embeddings-benchmark-mteb) - 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/_