--- title: "mteb vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/embeddings-benchmark-mteb-vs-wangrongsheng-awesome-llm-resources" tools: ["embeddings-benchmark-mteb", "wangrongsheng-awesome-llm-resources"] --- # mteb vs awesome-LLM-resources *GraphCanon updated Aug 22, 2026* ## Verdict Pick mteb if mTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a. [mteb](https://docs.mteb.org) reports 3.4k GitHub stars, 670 forks, and 340 open issues, last pushed Aug 21, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [mteb's repository](https://github.com/embeddings-benchmark/mteb) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [mteb](/tools/embeddings-benchmark-mteb.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | State-of-the-art evaluation of embeddings across languages and modalities | Summary of the world's best LLM resources. | | Stars | 3,400 | 8,845 | | Forks | 670 | 950 | | Open issues | 340 | 23 | | Language | Python | - | | Adopt for | MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license. | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mteb](/tools/embeddings-benchmark-mteb.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 340 | 23 | | Stars delta | +36 (30d) | +142 (30d) | | Open issues delta | +31 (30d) | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/embeddings-benchmark-mteb/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: mteb - **Adopt for:** MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license. ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose mteb if… - Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings. - mteb ships Docker support for self-hosted deployment. - You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts. ### Choose awesome-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models. - Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks, Model Training. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use mteb - Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope. - You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between mteb and awesome-LLM-resources? mteb: State-of-the-art evaluation of embeddings across languages and modalities. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose mteb over awesome-LLM-resources? Choose mteb over awesome-LLM-resources when Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings; mteb ships Docker support for self-hosted deployment; You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts. ### When should I choose awesome-LLM-resources over mteb? Choose awesome-LLM-resources over mteb when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks, Model Training; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid mteb? Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope. You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is mteb or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 3,400). Stars measure visibility, not whether either tool fits your constraints. ### Are mteb and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (mteb: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to mteb or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [mteb alternatives](/tools/embeddings-benchmark-mteb/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([mteb markdown twin](/tools/embeddings-benchmark-mteb/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mteb or awesome-LLM-resources? mteb: Very active. awesome-LLM-resources: Very active. 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 awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mteb trust report](/tools/embeddings-benchmark-mteb/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/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/_