--- title: "VLMEvalKit vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/open-compass-vlmevalkit-vs-wangrongsheng-awesome-llm-resources" tools: ["open-compass-vlmevalkit", "wangrongsheng-awesome-llm-resources"] --- # VLMEvalKit vs awesome-LLM-resources *GraphCanon updated Sep 20, 2026* ## Verdict Pick VLMEvalKit if vLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [VLMEvalKit](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard) reports 4.3k GitHub stars, 745 forks, and 285 open issues, last pushed Aug 17, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [VLMEvalKit's repository](https://github.com/open-compass/VLMEvalKit) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [VLMEvalKit](/tools/open-compass-vlmevalkit.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | An open-source evaluation toolkit for large vision-language models | Summary of the world's best LLM resources. | | Stars | 4,345 | 8,968 | | Forks | 745 | 993 | | Open issues | 285 | 40 | | Language | Python | - | | Adopt for | VLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | Evaluation & Observability | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [VLMEvalKit](/tools/open-compass-vlmevalkit.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Days since push | 0d | 3d | | Open issues (now) | 285 | 40 | | Stars delta | +60 (30d) | +123 (30d) | | Open issues delta | +21 (30d) | +17 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/open-compass-vlmevalkit/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: VLMEvalKit - **Adopt for:** VLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models. ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose VLMEvalKit if… - Tags unique to VLMEvalKit: computer-vision, evaluation, multi-modal. - When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy. ### Choose awesome-LLM-resources if… - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, llama. - Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Inference & Serving, LLM Frameworks, Model Training. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use VLMEvalKit - If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format. - When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction. ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between VLMEvalKit and awesome-LLM-resources? VLMEvalKit: An open-source evaluation toolkit for large vision-language models. 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 VLMEvalKit over awesome-LLM-resources? Choose VLMEvalKit over awesome-LLM-resources when Tags unique to VLMEvalKit: computer-vision, evaluation, multi-modal; When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy. ### When should I choose awesome-LLM-resources over VLMEvalKit? Choose awesome-LLM-resources over VLMEvalKit when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, llama; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Inference & Serving, LLM Frameworks, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid VLMEvalKit? If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format. When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction. ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is VLMEvalKit or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,968 vs 4,345). Stars measure visibility, not whether either tool fits your constraints. ### Are VLMEvalKit and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (VLMEvalKit: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to VLMEvalKit or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [VLMEvalKit alternatives](/tools/open-compass-vlmevalkit/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([VLMEvalKit markdown twin](/tools/open-compass-vlmevalkit/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/open-compass-vlmevalkit-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, VLMEvalKit or awesome-LLM-resources? VLMEvalKit: Very active. 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 VLMEvalKit and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [VLMEvalKit trust report](/tools/open-compass-vlmevalkit/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=open-compass-vlmevalkit`](/api/graphcanon/graph?tool=open-compass-vlmevalkit) - 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/_