--- title: "DeepSeek-R1 vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-mlabonne-llm-course" tools: ["deepseek-ai-deepseek-r1", "mlabonne-llm-course"] --- # DeepSeek-R1 vs llm-course *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 llm-course if the llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 91,991 | 80,839 | | Forks | 11,711 | 9,421 | | Open issues | 45 | 84 | | Language | - | - | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 379d | 155d | | Open issues (now) | 45 | 84 | | Owner type | Organization | User | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/mlabonne-llm-course/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: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, llm-course 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. - 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 llm-course if… - License: llm-course is Apache-2.0, DeepSeek-R1 is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large language models, machine learning. - Also covers Evaluation & Observability, Inference & Serving. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## 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 llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between DeepSeek-R1 and llm-course? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over llm-course? Choose DeepSeek-R1 over llm-course when License: DeepSeek-R1 is MIT, llm-course 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; 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 llm-course over DeepSeek-R1? Choose llm-course over DeepSeek-R1 when License: llm-course is Apache-2.0, DeepSeek-R1 is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large language models, machine learning; Also covers Evaluation & Observability, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### 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 llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is DeepSeek-R1 or llm-course more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 80,839). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and llm-course open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to DeepSeek-R1 or llm-course? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or llm-course? DeepSeek-R1: Dormant. llm-course: 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 DeepSeek-R1 and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [llm-course trust report](/tools/mlabonne-llm-course/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/_