--- title: "mteb vs chain-of-thought-hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/embeddings-benchmark-mteb-vs-franxyao-chain-of-thought-hub" tools: ["embeddings-benchmark-mteb", "franxyao-chain-of-thought-hub"] --- # mteb vs chain-of-thought-hub *GraphCanon updated Jul 12, 2026* ## Verdict Pick mteb when mteb is primarily Python; chain-of-thought-hub is Jupyter Notebook; pick chain-of-thought-hub when chain-of-thought-hub is primarily Jupyter Notebook; mteb is Python. [mteb](https://docs.mteb.org) reports 3.3k GitHub stars, 638 forks, and 295 open issues, last pushed Jul 9, 2026. [chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub) has 2.8k stars, 144 forks, and 27 open issues, last pushed Aug 4, 2024. Figures are from public GitHub metadata via [mteb's repository](https://github.com/embeddings-benchmark/mteb) and [chain-of-thought-hub's repository](https://github.com/FranxYao/chain-of-thought-hub). | | [mteb](/tools/embeddings-benchmark-mteb.md) | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | | --- | --- | --- | | Tagline | State-of-the-art evaluation of embeddings across languages and modalities | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | | Stars | 3,349 | 2,777 | | Forks | 638 | 144 | | Open issues | 295 | 27 | | Language | Python | Jupyter Notebook | | 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 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mteb](/tools/embeddings-benchmark-mteb.md) | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 706d | | Open issues (now) | 295 | 27 | | Owner type | Organization | User | | Full report | [trust report](/tools/embeddings-benchmark-mteb/trust.md) | [trust report](/tools/franxyao-chain-of-thought-hub/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. ## Choose when ### Choose mteb if… - mteb is primarily Python; chain-of-thought-hub is Jupyter Notebook. - License: mteb is Apache-2.0, chain-of-thought-hub is MIT. - Tags unique to mteb: benchmark, embeddings, evaluation, information-retrieval. - mteb ships Docker support for self-hosted deployment. ### Choose chain-of-thought-hub if… - chain-of-thought-hub is primarily Jupyter Notebook; mteb is Python. - License: chain-of-thought-hub is MIT, mteb is Apache-2.0. - 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: chain-of-thought prompting, complex reasoning, llm-benchmarking. - Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ## 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 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. ## Common questions ### What is the difference between mteb and chain-of-thought-hub? mteb: State-of-the-art evaluation of embeddings across languages and modalities. chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting. See the comparison table for live GitHub stats and shared categories. ### When should I choose mteb over chain-of-thought-hub? Choose mteb over chain-of-thought-hub when mteb is primarily Python; chain-of-thought-hub is Jupyter Notebook; License: mteb is Apache-2.0, chain-of-thought-hub is MIT; Tags unique to mteb: benchmark, embeddings, evaluation, information-retrieval; mteb ships Docker support for self-hosted deployment. ### When should I choose chain-of-thought-hub over mteb? Choose chain-of-thought-hub over mteb when chain-of-thought-hub is primarily Jupyter Notebook; mteb is Python; License: chain-of-thought-hub is MIT, mteb is Apache-2.0; 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: chain-of-thought prompting, complex reasoning, llm-benchmarking; Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ### 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 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. ### Is mteb or chain-of-thought-hub more popular on GitHub? mteb has more GitHub stars (3,349 vs 2,777). Stars measure visibility, not whether either tool fits your constraints. ### Are mteb and chain-of-thought-hub open source? Yes - both are open-source projects on GitHub (mteb: Apache-2.0, chain-of-thought-hub: MIT). ### Where can I find alternatives to mteb or chain-of-thought-hub? GraphCanon lists graph-backed alternatives at [mteb alternatives](/tools/embeddings-benchmark-mteb/alternatives) and [chain-of-thought-hub alternatives](/tools/franxyao-chain-of-thought-hub/alternatives) ([mteb markdown twin](/tools/embeddings-benchmark-mteb/alternatives.md), [chain-of-thought-hub markdown twin](/tools/franxyao-chain-of-thought-hub/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-franxyao-chain-of-thought-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mteb or chain-of-thought-hub? mteb: Very active. chain-of-thought-hub: 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 chain-of-thought-hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mteb trust report](/tools/embeddings-benchmark-mteb/trust); [chain-of-thought-hub trust report](/tools/franxyao-chain-of-thought-hub/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/_