--- title: "Awesome-LLM-3D vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activevisionlab-awesome-llm-3d-vs-onejune2018-awesome-llm-eval" tools: ["activevisionlab-awesome-llm-3d", "onejune2018-awesome-llm-eval"] --- # Awesome-LLM-3D vs Awesome-LLM-Eval *GraphCanon updated Aug 6, 2026* ## Verdict Pick Awesome-LLM-3D if awesome-LLM-3D is a curated list of multi-modal large language model resources dedicated to tasks in the 3D domain, including areas such as unified understanding, reasoning, and embodied agents; pick Awesome-LLM-Eval if awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. [Awesome-LLM-3D](https://github.com/ActiveVisionLab/Awesome-LLM-3D) reports 2.2k GitHub stars, 143 forks, and 7 open issues, last pushed Apr 16, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 654 stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [Awesome-LLM-3D's repository](https://github.com/ActiveVisionLab/Awesome-LLM-3D) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [Awesome-LLM-3D](/tools/activevisionlab-awesome-llm-3d.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | Curated list of Multi-modal Large Language Model resources for 3D world tasks | Curated list for evaluation of large language models | | Stars | 2,246 | 654 | | Forks | 143 | 82 | | Open issues | 7 | 44 | | Language | - | - | | Adopt for | Awesome-LLM-3D is a curated list of multi-modal large language model resources dedicated to tasks in the 3D domain, including areas such as unified understanding, reasoning, and embodied agents. | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | | Persona | - | - | | Runtime | - | - | | License | The tool is licensed under MIT, allowing free use for both personal and commercial projects with appropriate attribution. | MIT | | Categories | Computer Vision, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-3D](/tools/activevisionlab-awesome-llm-3d.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Days since push | 112d | 246d | | Open issues (now) | 7 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/activevisionlab-awesome-llm-3d/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## Decision facts: Awesome-LLM-3D - **Requirements:** - This repository does not require Docker or specific dependencies. It is a curated list of resources intended for researchers and developers interested in the - **Adopt for:** Awesome-LLM-3D is a curated list of multi-modal large language model resources dedicated to tasks in the 3D domain, including areas such as unified understanding, reasoning, and embodied agents. - **License detail:** The tool is licensed under MIT, allowing free use for both personal and commercial projects with appropriate attribution. ## Decision facts: Awesome-LLM-Eval - **Pricing:** freemium - The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms. - **Requirements:** The resources listed may vary in their own requirements, including software dependencies and hardware specifications. - **Adopt for:** Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. ## Choose when ### Choose Awesome-LLM-3D if… - Requirements: - This repository does not require Docker or specific dependencies. It is a curated list of resources intended for researchers and developers interested in the . - Tags unique to Awesome-LLM-3D: 3d understanding, embodied agents, foundation-models, generation. - Also covers Computer Vision, Model Training. - - When you are looking for specific and updated information on how LLMs can be applied to various 3D tasks like understanding, generation, and embodied agents. ### Choose Awesome-LLM-Eval if… - Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.. - Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.. - Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation. - Also covers Evaluation & Observability. - When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment. ## When NOT to use Awesome-LLM-3D - - If you are seeking real-time applications or tools for immediate use case deployment rather than a curated list of research papers and resources. - - Avoid if your focus is on more general computer vision tasks that do not specifically involve multi-modal LLMs within the 3D domain. ## When NOT to use Awesome-LLM-Eval - You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. - If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool. ## Common questions ### What is the difference between Awesome-LLM-3D and Awesome-LLM-Eval? Awesome-LLM-3D: Curated list of Multi-modal Large Language Model resources for 3D world tasks. Awesome-LLM-Eval: Curated list for evaluation of large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-3D over Awesome-LLM-Eval? Choose Awesome-LLM-3D over Awesome-LLM-Eval when Requirements: - This repository does not require Docker or specific dependencies. It is a curated list of resources intended for researchers and developers interested in the ; Tags unique to Awesome-LLM-3D: 3d understanding, embodied agents, foundation-models, generation; Also covers Computer Vision, Model Training; - When you are looking for specific and updated information on how LLMs can be applied to various 3D tasks like understanding, generation, and embodied agents. ### When should I choose Awesome-LLM-Eval over Awesome-LLM-3D? Choose Awesome-LLM-Eval over Awesome-LLM-3D when Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.; Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.; Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation; Also covers Evaluation & Observability; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment. ### When should I avoid Awesome-LLM-3D? - If you are seeking real-time applications or tools for immediate use case deployment rather than a curated list of research papers and resources. - Avoid if your focus is on more general computer vision tasks that do not specifically involve multi-modal LLMs within the 3D domain. ### When should I avoid Awesome-LLM-Eval? You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool. ### Is Awesome-LLM-3D or Awesome-LLM-Eval more popular on GitHub? Awesome-LLM-3D has more GitHub stars (2,246 vs 654). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-3D and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub (Awesome-LLM-3D: MIT, Awesome-LLM-Eval: MIT). ### Where can I find alternatives to Awesome-LLM-3D or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [Awesome-LLM-3D alternatives](/tools/activevisionlab-awesome-llm-3d/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([Awesome-LLM-3D markdown twin](/tools/activevisionlab-awesome-llm-3d/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/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/activevisionlab-awesome-llm-3d-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-3D or Awesome-LLM-Eval? Awesome-LLM-3D: Slowing. Awesome-LLM-Eval: Slowing. 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 Awesome-LLM-3D and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-3D trust report](/tools/activevisionlab-awesome-llm-3d/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activevisionlab-awesome-llm-3d`](/api/graphcanon/graph?tool=activevisionlab-awesome-llm-3d) - 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/_