--- title: "DeepSeek-R1 vs DeepSeek-V3" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-deepseek-ai-deepseek-v3" tools: ["deepseek-ai-deepseek-r1", "deepseek-ai-deepseek-v3"] --- # DeepSeek-R1 vs DeepSeek-V3 *GraphCanon updated Aug 6, 2026* ## Verdict Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick DeepSeek-V3 if deepSeek-V3 is a Python-based AI development tool, with documentation focused solely on licensing terms for both its codebase and models. It's unclear from the available information what specific features or capabilities. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 38 open issues, last pushed Jun 27, 2025. [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) has 104k stars, 17k forks, and 214 open issues, last pushed Aug 28, 2025. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [DeepSeek-V3's repository](https://github.com/deepseek-ai/DeepSeek-V3). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [DeepSeek-V3](/tools/deepseek-ai-deepseek-v3.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Repository lacking description with unspecified content related to AI development. | | Stars | 91,982 | 104,121 | | Forks | 11,706 | 16,726 | | Open issues | 38 | 214 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | DeepSeek-V3 is a Python-based AI development tool, with documentation focused solely on licensing terms for both its codebase and models. It's unclear from the available information what specific features or capabilities | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Model Training | Developer Tools, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [DeepSeek-V3](/tools/deepseek-ai-deepseek-v3.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 405d | 343d | | Open issues (now) | 38 | 214 | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/deepseek-ai-deepseek-v3/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: DeepSeek-V3 - **Adopt for:** DeepSeek-V3 is a Python-based AI development tool, with documentation focused solely on licensing terms for both its codebase and models. It's unclear from the available information what specific features or capabilities ## 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: derived models, distilled models. - Also covers LLM Frameworks, Model Training. - 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 DeepSeek-V3 if… - Tags unique to DeepSeek-V3: python. - Also covers Developer Tools, Inference & Serving. - - When you need an AI model that allows for commercial usage as DeepSeek-V3 explicitly supports this based on licensing provided. ## 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 DeepSeek-V3 - - If detailed documentation and clear feature descriptions are crucial as the repository lacks descriptive content. - - When you require open-source model details or functionalities other than those related solely to licensing terms. ## Common questions ### What is the difference between DeepSeek-R1 and DeepSeek-V3? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. DeepSeek-V3: Repository lacking description with unspecified content related to AI development.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over DeepSeek-V3? Choose DeepSeek-R1 over DeepSeek-V3 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: derived models, distilled models; Also covers LLM Frameworks, Model Training; 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 DeepSeek-V3 over DeepSeek-R1? Choose DeepSeek-V3 over DeepSeek-R1 when Tags unique to DeepSeek-V3: python; Also covers Developer Tools, Inference & Serving; - When you need an AI model that allows for commercial usage as DeepSeek-V3 explicitly supports this based on licensing provided. ### 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 DeepSeek-V3? - If detailed documentation and clear feature descriptions are crucial as the repository lacks descriptive content. - When you require open-source model details or functionalities other than those related solely to licensing terms. ### Is DeepSeek-R1 or DeepSeek-V3 more popular on GitHub? DeepSeek-V3 has more GitHub stars (104,121 vs 91,982). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and DeepSeek-V3 open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, DeepSeek-V3: MIT). ### Where can I find alternatives to DeepSeek-R1 or DeepSeek-V3? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [DeepSeek-V3 alternatives](/tools/deepseek-ai-deepseek-v3/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [DeepSeek-V3 markdown twin](/tools/deepseek-ai-deepseek-v3/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-deepseek-ai-deepseek-v3.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or DeepSeek-V3? DeepSeek-R1: Dormant. DeepSeek-V3: 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 DeepSeek-V3? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [DeepSeek-V3 trust report](/tools/deepseek-ai-deepseek-v3/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/_