--- title: "BIG-bench vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-big-bench-vs-wangrongsheng-awesome-llm-resources" tools: ["google-big-bench", "wangrongsheng-awesome-llm-resources"] --- # BIG-bench vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick BIG-bench if decision-critical facts for BIG-bench; 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. [BIG-bench](https://github.com/google/BIG-bench) reports 3.2k GitHub stars, 617 forks, and 106 open issues, last pushed Jul 19, 2024. [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 [BIG-bench's repository](https://github.com/google/BIG-bench) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [BIG-bench](/tools/google-big-bench.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Collaborative benchmark for language model capabilities | Summary of the world's best LLM resources. | | Stars | 3,249 | 8,845 | | Forks | 617 | 950 | | Open issues | 106 | 23 | | Language | Python | - | | Adopt for | Decision-critical facts for BIG-bench | 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._ | | [BIG-bench](/tools/google-big-bench.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 748d | 2d | | Archived on GitHub | Yes | No | | Open issues (now) | 106 | 23 | | Stars delta | Unknown | +142 (30d) | | Open issues delta | Unknown | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/google-big-bench/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: BIG-bench - **Requirements:** Python 3.5-3.8 required.; `pytest` is necessary for running automated tests. - **Adopt for:** Decision-critical facts for BIG-bench ## 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 BIG-bench if… - Requirements: Python 3.5-3.8 required.; `pytest` is necessary for running automated tests.. - Tags unique to BIG-bench: benchmarking, evaluation, language-models, seqio. - When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities. ### 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 BIG-bench - If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools. - As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential - If your project does not require advanced extrapolation techniques for measuring model capabilities over a wide range of benchmarks, simpler evaluation tools may suffice. ## 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 BIG-bench and awesome-LLM-resources? BIG-bench: Collaborative benchmark for language model capabilities. 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 BIG-bench over awesome-LLM-resources? Choose BIG-bench over awesome-LLM-resources when Requirements: Python 3.5-3.8 required.; `pytest` is necessary for running automated tests.; Tags unique to BIG-bench: benchmarking, evaluation, language-models, seqio; When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities. ### When should I choose awesome-LLM-resources over BIG-bench? Choose awesome-LLM-resources over BIG-bench 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 BIG-bench? If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools. As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential If your project does not require advanced extrapolation techniques for measuring model capabilities over a wide range of benchmarks, simpler evaluation tools may suffice. ### 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 BIG-bench or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 3,249). Stars measure visibility, not whether either tool fits your constraints. ### Are BIG-bench and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (BIG-bench: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to BIG-bench or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [BIG-bench alternatives](/tools/google-big-bench/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([BIG-bench markdown twin](/tools/google-big-bench/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/google-big-bench-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, BIG-bench or awesome-LLM-resources? BIG-bench: Archived. 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 BIG-bench and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [BIG-bench trust report](/tools/google-big-bench/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-big-bench`](/api/graphcanon/graph?tool=google-big-bench) - 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/_