--- title: "DeepSeek-R1 vs vlms-zero-to-hero" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-skalskip-vlms-zero-to-hero" tools: ["deepseek-ai-deepseek-r1", "skalskip-vlms-zero-to-hero"] --- # DeepSeek-R1 vs vlms-zero-to-hero *GraphCanon updated Jul 12, 2026* ## Verdict Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick vlms-zero-to-hero if a comprehensive guide for those seeking a deep understanding of NLP and CV leading to advanced Vision-Language models. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [vlms-zero-to-hero](https://www.youtube.com/@SkalskiP) has 1.2k stars, 103 forks, and 1 open issues, last pushed Jan 23, 2025. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [vlms-zero-to-hero's repository](https://github.com/SkalskiP/vlms-zero-to-hero). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [vlms-zero-to-hero](/tools/skalskip-vlms-zero-to-hero.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Journey from NLP fundamentals to Vision-Language Models | | Stars | 91,991 | 1,181 | | Forks | 11,711 | 103 | | Open issues | 45 | 1 | | Language | - | Jupyter Notebook | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | A comprehensive guide for those seeking a deep understanding of NLP and CV leading to advanced Vision-Language models. | | Persona | - | - | | Runtime | - | - | | License | MIT | The 'vlms-zero-to-hero' repository is licensed under Apache-2.0 which allows for free use, modification and distribution. | | Categories | LLM Frameworks, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [vlms-zero-to-hero](/tools/skalskip-vlms-zero-to-hero.md) | | --- | --- | --- | | Days since push | 379d | 534d | | Open issues (now) | 45 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/skalskip-vlms-zero-to-hero/trust.md) | ## Decision facts: DeepSeek-R1 - **Pricing:** freemium - The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository. - **Requirements:** Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs. - **Adopt for:** DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. ## Decision facts: vlms-zero-to-hero - **Pricing:** freemium - Free to use with no hidden costs due to its open-source nature. - **Requirements:** Requires a basic understanding of Python. Access to Jupyter Notebook is necessary. - **Adopt for:** A comprehensive guide for those seeking a deep understanding of NLP and CV leading to advanced Vision-Language models. - **License detail:** The 'vlms-zero-to-hero' repository is licensed under Apache-2.0 which allows for free use, modification and distribution. ## Choose when ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, vlms-zero-to-hero is Apache-2.0. - Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.. - Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.. - Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license. - Also covers LLM Frameworks. - When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### Choose vlms-zero-to-hero if… - License: vlms-zero-to-hero is Apache-2.0, DeepSeek-R1 is MIT. - Pricing: Free to use with no hidden costs due to its open-source nature.. - Requirements: Requires a basic understanding of Python. Access to Jupyter Notebook is necessary.. - Tags unique to vlms-zero-to-hero: bert-model, clip, computer-vision, embeddings. - Also covers Computer Vision. - Use 'vlms-zero-to-hero' when you want an in-depth, step-by-step introduction that ranges from foundational NLP and CV concepts up to advanced Vision-Language models. ## When NOT to use DeepSeek-R1 - Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. - If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ## When NOT to use vlms-zero-to-hero - Avoid 'vlms-zero-to-hero' if you have an advanced background in both NLP and Vision-Language Models and are looking for immediate hands-on experience rather than theoretical depth. - Do not use this tool if you require a quick solution or implementation of vision-language models, as it emphasizes comprehensive learning and conceptual understanding. ## Common questions ### What is the difference between DeepSeek-R1 and vlms-zero-to-hero? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. vlms-zero-to-hero: Journey from NLP fundamentals to Vision-Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over vlms-zero-to-hero? Choose DeepSeek-R1 over vlms-zero-to-hero when License: DeepSeek-R1 is MIT, vlms-zero-to-hero is Apache-2.0; Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license; Also covers LLM Frameworks; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### When should I choose vlms-zero-to-hero over DeepSeek-R1? Choose vlms-zero-to-hero over DeepSeek-R1 when License: vlms-zero-to-hero is Apache-2.0, DeepSeek-R1 is MIT; Pricing: Free to use with no hidden costs due to its open-source nature.; Requirements: Requires a basic understanding of Python. Access to Jupyter Notebook is necessary.; Tags unique to vlms-zero-to-hero: bert-model, clip, computer-vision, embeddings; Also covers Computer Vision; Use 'vlms-zero-to-hero' when you want an in-depth, step-by-step introduction that ranges from foundational NLP and CV concepts up to advanced Vision-Language models. ### When should I avoid DeepSeek-R1? Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ### When should I avoid vlms-zero-to-hero? Avoid 'vlms-zero-to-hero' if you have an advanced background in both NLP and Vision-Language Models and are looking for immediate hands-on experience rather than theoretical depth. Do not use this tool if you require a quick solution or implementation of vision-language models, as it emphasizes comprehensive learning and conceptual understanding. ### Is DeepSeek-R1 or vlms-zero-to-hero more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 1,181). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and vlms-zero-to-hero open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, vlms-zero-to-hero: Apache-2.0). ### Where can I find alternatives to DeepSeek-R1 or vlms-zero-to-hero? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [vlms-zero-to-hero alternatives](/tools/skalskip-vlms-zero-to-hero/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [vlms-zero-to-hero markdown twin](/tools/skalskip-vlms-zero-to-hero/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/deepseek-ai-deepseek-r1-vs-skalskip-vlms-zero-to-hero.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or vlms-zero-to-hero? DeepSeek-R1: Dormant. vlms-zero-to-hero: Dormant. 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 DeepSeek-R1 and vlms-zero-to-hero? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [vlms-zero-to-hero trust report](/tools/skalskip-vlms-zero-to-hero/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1`](/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1) - 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/_