--- title: "DeepSeek-R1 vs knowledge-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-geeks-of-data-knowledge-gpt" tools: ["deepseek-ai-deepseek-r1", "geeks-of-data-knowledge-gpt"] --- # DeepSeek-R1 vs knowledge-gpt *GraphCanon updated Jul 12, 2026* ## Verdict 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.; pick knowledge-gpt when tags unique to knowledge-gpt: context, embedding, embedding-vectors, gpt. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [knowledge-gpt](https://pypi.org/project/knowledgegpt/) has 291 stars, 52 forks, and 8 open issues, last pushed Apr 25, 2023. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [knowledge-gpt's repository](https://github.com/geeks-of-data/knowledge-gpt). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Extract knowledge from various sources and perform Q&A sessions using GPT models | | Stars | 91,991 | 291 | | Forks | 11,711 | 52 | | Open issues | 45 | 8 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Model Training | Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | | --- | --- | --- | | Days since push | 379d | 1173d | | Open issues (now) | 45 | 8 | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/geeks-of-data-knowledge-gpt/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 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. - 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 knowledge-gpt if… - Tags unique to knowledge-gpt: context, embedding, embedding-vectors, gpt. - Also covers Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving. - knowledge-gpt ships Docker support for self-hosted deployment. ## 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 knowledge-gpt - Last GitHub push was 1174 days ago (dormant maintenance, Apr 25, 2023). Validate activity before betting a new project on knowledge-gpt. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between DeepSeek-R1 and knowledge-gpt? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. knowledge-gpt: Extract knowledge from various sources and perform Q&A sessions using GPT models. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over knowledge-gpt? Choose DeepSeek-R1 over knowledge-gpt 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; 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 knowledge-gpt over DeepSeek-R1? Choose knowledge-gpt over DeepSeek-R1 when Tags unique to knowledge-gpt: context, embedding, embedding-vectors, gpt; Also covers Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving; knowledge-gpt ships Docker support for self-hosted deployment. ### 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 knowledge-gpt? Last GitHub push was 1174 days ago (dormant maintenance, Apr 25, 2023). Validate activity before betting a new project on knowledge-gpt. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is DeepSeek-R1 or knowledge-gpt more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 291). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and knowledge-gpt open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, knowledge-gpt: MIT). ### Where can I find alternatives to DeepSeek-R1 or knowledge-gpt? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [knowledge-gpt alternatives](/tools/geeks-of-data-knowledge-gpt/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [knowledge-gpt markdown twin](/tools/geeks-of-data-knowledge-gpt/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-geeks-of-data-knowledge-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or knowledge-gpt? DeepSeek-R1: Dormant. knowledge-gpt: 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 knowledge-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [knowledge-gpt trust report](/tools/geeks-of-data-knowledge-gpt/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/_