--- title: "FLsystem-paper vs DeepSeek-R1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amberljc-flsystem-paper-vs-deepseek-ai-deepseek-r1" tools: ["amberljc-flsystem-paper", "deepseek-ai-deepseek-r1"] --- # FLsystem-paper vs DeepSeek-R1 *GraphCanon updated Jul 12, 2026* ## Verdict Pick FLsystem-paper when tags unique to FLsystem-paper: awesome-list, federated-learning, machine-learning, mlsys; pick DeepSeek-R1 when pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.. [FLsystem-paper](https://github.com/AmberLJC/FLsystem-paper) reports 75 GitHub stars, 7 forks, and 1 open issues, last pushed Feb 7, 2024. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) has 92k stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. Figures are from public GitHub metadata via [FLsystem-paper's repository](https://github.com/AmberLJC/FLsystem-paper) and [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1). | | [FLsystem-paper](/tools/amberljc-flsystem-paper.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Tagline | Federated Learning Systems Paper List | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | | Stars | 75 | 91,991 | | Forks | 7 | 11,711 | | Open issues | 1 | 45 | | Language | - | - | | Adopt for | - | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FLsystem-paper](/tools/amberljc-flsystem-paper.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Days since push | 885d | 379d | | Open issues (now) | 1 | 45 | | Owner type | User | Organization | | Full report | [trust report](/tools/amberljc-flsystem-paper/trust.md) | [trust report](/tools/deepseek-ai-deepseek-r1/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. ## Choose when ### Choose FLsystem-paper if… - Tags unique to FLsystem-paper: awesome-list, federated-learning, machine-learning, mlsys. - Also covers Inference & Serving. - Leaner open-issue backlog (1). ### Choose DeepSeek-R1 if… - 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 NOT to use FLsystem-paper - Last GitHub push was 886 days ago (dormant maintenance, Feb 7, 2024). Validate activity before betting a new project on FLsystem-paper. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing 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. ## Common questions ### What is the difference between FLsystem-paper and DeepSeek-R1? FLsystem-paper: Federated Learning Systems Paper List. DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FLsystem-paper over DeepSeek-R1? Choose FLsystem-paper over DeepSeek-R1 when Tags unique to FLsystem-paper: awesome-list, federated-learning, machine-learning, mlsys; Also covers Inference & Serving; Leaner open-issue backlog (1). ### When should I choose DeepSeek-R1 over FLsystem-paper? Choose DeepSeek-R1 over FLsystem-paper when 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 avoid FLsystem-paper? Last GitHub push was 886 days ago (dormant maintenance, Feb 7, 2024). Validate activity before betting a new project on FLsystem-paper. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing 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. ### Is FLsystem-paper or DeepSeek-R1 more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 75). Stars measure visibility, not whether either tool fits your constraints. ### Are FLsystem-paper and DeepSeek-R1 open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to FLsystem-paper or DeepSeek-R1? GraphCanon lists graph-backed alternatives at [FLsystem-paper alternatives](/tools/amberljc-flsystem-paper/alternatives) and [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) ([FLsystem-paper markdown twin](/tools/amberljc-flsystem-paper/alternatives.md), [DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/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/amberljc-flsystem-paper-vs-deepseek-ai-deepseek-r1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FLsystem-paper or DeepSeek-R1? FLsystem-paper: Dormant. DeepSeek-R1: 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 FLsystem-paper and DeepSeek-R1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FLsystem-paper trust report](/tools/amberljc-flsystem-paper/trust); [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amberljc-flsystem-paper`](/api/graphcanon/graph?tool=amberljc-flsystem-paper) - 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/_