--- title: "chain-of-thought-hub vs simple-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/franxyao-chain-of-thought-hub-vs-openai-simple-evals" tools: ["franxyao-chain-of-thought-hub", "openai-simple-evals"] --- # chain-of-thought-hub vs simple-evals *GraphCanon updated Jul 11, 2026* ## Verdict Pick chain-of-thought-hub when chain-of-thought-hub is primarily Jupyter Notebook; simple-evals is Python; pick simple-evals when simple-evals is primarily Python; chain-of-thought-hub is Jupyter Notebook. [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. [simple-evals](https://github.com/openai/simple-evals) has 4.6k stars, 493 forks, and 56 open issues, last pushed Apr 22, 2026. Figures are from public GitHub metadata via [chain-of-thought-hub's repository](https://github.com/FranxYao/chain-of-thought-hub) and [simple-evals's repository](https://github.com/openai/simple-evals). | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Tagline | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | simple-evals | | Stars | 2,777 | 4,565 | | Forks | 144 | 493 | | Open issues | 27 | 56 | | 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 | - | | Persona | - | - | | Runtime | - | - | | License | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | MIT | | Categories | Evaluation & Observability | LLM Frameworks, 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) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 706d | 79d | | Open issues (now) | 27 | 56 | | Owner type | User | Organization | | Full report | [trust report](/tools/franxyao-chain-of-thought-hub/trust.md) | [trust report](/tools/openai-simple-evals/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 chain-of-thought-hub if… - chain-of-thought-hub is primarily Jupyter Notebook; simple-evals is Python. - 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: complex reasoning, chain-of-thought prompting, llm-benchmarking. - Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ### Choose simple-evals if… - simple-evals is primarily Python; chain-of-thought-hub is Jupyter Notebook. - Tags unique to simple-evals: python. - Also covers LLM Frameworks. ## 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 simple-evals - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between chain-of-thought-hub and simple-evals? chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting. simple-evals: simple-evals. See the comparison table for live GitHub stats and shared categories. ### When should I choose chain-of-thought-hub over simple-evals? Choose chain-of-thought-hub over simple-evals when chain-of-thought-hub is primarily Jupyter Notebook; simple-evals is Python; 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: complex reasoning, chain-of-thought prompting, 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 simple-evals over chain-of-thought-hub? Choose simple-evals over chain-of-thought-hub when simple-evals is primarily Python; chain-of-thought-hub is Jupyter Notebook; Tags unique to simple-evals: python; Also covers LLM Frameworks. ### 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 simple-evals? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is chain-of-thought-hub or simple-evals more popular on GitHub? simple-evals has more GitHub stars (4,565 vs 2,777). Stars measure visibility, not whether either tool fits your constraints. ### Are chain-of-thought-hub and simple-evals open source? Yes - both are open-source projects on GitHub (chain-of-thought-hub: MIT, simple-evals: MIT). ### Where can I find alternatives to chain-of-thought-hub or simple-evals? GraphCanon lists graph-backed alternatives at [chain-of-thought-hub alternatives](/tools/franxyao-chain-of-thought-hub/alternatives) and [simple-evals alternatives](/tools/openai-simple-evals/alternatives) ([chain-of-thought-hub markdown twin](/tools/franxyao-chain-of-thought-hub/alternatives.md), [simple-evals markdown twin](/tools/openai-simple-evals/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-openai-simple-evals.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 simple-evals? chain-of-thought-hub: Dormant. simple-evals: Steady. 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 simple-evals? 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); [simple-evals trust report](/tools/openai-simple-evals/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/_