--- title: "chain-of-thought-hub vs cupel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/franxyao-chain-of-thought-hub-vs-tolitius-cupel" tools: ["franxyao-chain-of-thought-hub", "tolitius-cupel"] --- # chain-of-thought-hub vs cupel *GraphCanon updated Sep 20, 2026* ## Verdict Pick chain-of-thought-hub if 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; pick cupel if cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues. [chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub) reports 2.8k GitHub stars, 144 forks, and 27 open issues, last pushed Aug 4, 2024. [cupel](https://cupel.run) has 64 stars, 0 forks, and 2 open issues, last pushed Aug 31, 2026. Figures are from public GitHub metadata via [chain-of-thought-hub's repository](https://github.com/FranxYao/chain-of-thought-hub) and [cupel's repository](https://github.com/tolitius/cupel). | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [cupel](/tools/tolitius-cupel.md) | | --- | --- | --- | | Tagline | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | discovery tool for evaluating LLM performance | | Stars | 2,774 | 64 | | Forks | 144 | 0 | | Open issues | 27 | 2 | | Language | Jupyter Notebook | Python | | 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 | Cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. | | Persona | - | - | | Runtime | - | - | | License | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [cupel](/tools/tolitius-cupel.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 762d | 10d | | Open issues (now) | 27 | 2 | | Stars delta | 0 (30d) | +13 (30d) | | Full report | [trust report](/tools/franxyao-chain-of-thought-hub/trust.md) | [trust report](/tools/tolitius-cupel/trust.md) | ## Shared compatibility - **Python**: [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) - Python runtime; [cupel](/tools/tolitius-cupel.md) - Python runtime ## 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. ## Decision facts: cupel - **Adopt for:** Cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. ## Choose when ### Choose chain-of-thought-hub if… - chain-of-thought-hub is primarily Jupyter Notebook; cupel is Python. - License: chain-of-thought-hub is MIT, cupel 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. ### Choose cupel if… - cupel is primarily Python; chain-of-thought-hub is Jupyter Notebook. - License: cupel is Apache-2.0, chain-of-thought-hub is MIT. - Tags unique to cupel: inference-servers-discovery, llm-evaluation, local-llm, multi-turn-dialogue. - When aiming to evaluate LLMs on local servers due to its auto-discovery feature for known ports of inference servers ## 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. ## When NOT to use cupel - If you require a solution that supports a non-JavaScript runtime environment, as Cupel is JavaScript-exclusive - When you need a tool without UI capabilities since Cupel's UI is bundled in the package and may not suit headless operations ## Common questions ### What is the difference between chain-of-thought-hub and cupel? chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting. cupel: discovery tool for evaluating LLM performance. See the comparison table for live GitHub stats and shared categories. ### When should I choose chain-of-thought-hub over cupel? Choose chain-of-thought-hub over cupel when chain-of-thought-hub is primarily Jupyter Notebook; cupel is Python; License: chain-of-thought-hub is MIT, cupel 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 choose cupel over chain-of-thought-hub? Choose cupel over chain-of-thought-hub when cupel is primarily Python; chain-of-thought-hub is Jupyter Notebook; License: cupel is Apache-2.0, chain-of-thought-hub is MIT; Tags unique to cupel: inference-servers-discovery, llm-evaluation, local-llm, multi-turn-dialogue; When aiming to evaluate LLMs on local servers due to its auto-discovery feature for known ports of inference servers. ### 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. ### When should I avoid cupel? If you require a solution that supports a non-JavaScript runtime environment, as Cupel is JavaScript-exclusive When you need a tool without UI capabilities since Cupel's UI is bundled in the package and may not suit headless operations ### Is chain-of-thought-hub or cupel more popular on GitHub? chain-of-thought-hub has more GitHub stars (2,774 vs 64). Stars measure visibility, not whether either tool fits your constraints. ### Are chain-of-thought-hub and cupel open source? Yes - both are open-source projects on GitHub (chain-of-thought-hub: MIT, cupel: Apache-2.0). ### Where can I find alternatives to chain-of-thought-hub or cupel? GraphCanon lists graph-backed alternatives at [chain-of-thought-hub alternatives](/tools/franxyao-chain-of-thought-hub/alternatives) and [cupel alternatives](/tools/tolitius-cupel/alternatives) ([chain-of-thought-hub markdown twin](/tools/franxyao-chain-of-thought-hub/alternatives.md), [cupel markdown twin](/tools/tolitius-cupel/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/franxyao-chain-of-thought-hub-vs-tolitius-cupel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, chain-of-thought-hub or cupel? chain-of-thought-hub: Dormant. cupel: 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 chain-of-thought-hub and cupel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chain-of-thought-hub trust report](/tools/franxyao-chain-of-thought-hub/trust); [cupel trust report](/tools/tolitius-cupel/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub`](/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub) - 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/_