--- title: "DeepSeek-R1 vs llm-action" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-liguodongiot-llm-action" tools: ["deepseek-ai-deepseek-r1", "liguodongiot-llm-action"] --- # DeepSeek-R1 vs llm-action *GraphCanon updated Aug 16, 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-action if llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 38 open issues, last pushed Jun 27, 2025. [llm-action](https://www.zhihu.com/column/c_1456193767213043713) has 25k stars, 2.8k forks, and 19 open issues, last pushed Jul 19, 2026. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [llm-action's repository](https://github.com/liguodongiot/llm-action). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [llm-action](/tools/liguodongiot-llm-action.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Aims to share large model technology principles and practical experience (large model engineering, application implementation) | | Stars | 91,982 | 24,898 | | Forks | 11,706 | 2,842 | | Open issues | 38 | 19 | | Language | - | HTML | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. | | Persona | - | - | | Runtime | - | - | | License | MIT | llm-action is open-source under the Apache-2.0 license. | | Categories | LLM Frameworks, Model Training | 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-action](/tools/liguodongiot-llm-action.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 405d | 28d | | Open issues (now) | 38 | 19 | | Stars delta | Unknown | +162 (30d) | | Open issues delta | Unknown | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/liguodongiot-llm-action/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-action - **Adopt for:** llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. - **License detail:** llm-action is open-source under the Apache-2.0 license. ## Choose when ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, llm-action 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-action if… - License: llm-action is Apache-2.0, DeepSeek-R1 is MIT. - Tags unique to llm-action: deployment, engineering, inference, large model. - Also covers Inference & Serving. - - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual ## 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-action - - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes. - - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but ## Common questions ### What is the difference between DeepSeek-R1 and llm-action? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. llm-action: Aims to share large model technology principles and practical experience (large model engineering, application implementation). See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over llm-action? Choose DeepSeek-R1 over llm-action when License: DeepSeek-R1 is MIT, llm-action 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-action over DeepSeek-R1? Choose llm-action over DeepSeek-R1 when License: llm-action is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to llm-action: deployment, engineering, inference, large model; Also covers Inference & Serving; - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual. ### 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-action? - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes. - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but ### Is DeepSeek-R1 or llm-action more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,982 vs 24,898). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and llm-action open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, llm-action: Apache-2.0). ### Where can I find alternatives to DeepSeek-R1 or llm-action? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [llm-action alternatives](/tools/liguodongiot-llm-action/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [llm-action markdown twin](/tools/liguodongiot-llm-action/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-liguodongiot-llm-action.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-action? DeepSeek-R1: Dormant. llm-action: 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 DeepSeek-R1 and llm-action? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [llm-action trust report](/tools/liguodongiot-llm-action/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/_