--- title: "DeepSeek-R1 vs rellm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-r2d4-rellm" tools: ["deepseek-ai-deepseek-r1", "r2d4-rellm"] --- # DeepSeek-R1 vs rellm *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 rellm if rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [rellm](https://github.com/r2d4/rellm) has 513 stars, 23 forks, and 5 open issues, last pushed Aug 10, 2023. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [rellm's repository](https://github.com/r2d4/rellm). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [rellm](/tools/r2d4-rellm.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Exact structure out of any language model completion | | Stars | 91,991 | 513 | | Forks | 11,711 | 23 | | Open issues | 45 | 5 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [rellm](/tools/r2d4-rellm.md) | | --- | --- | --- | | Days since push | 379d | 1065d | | Open issues (now) | 45 | 5 | | Owner type | Organization | User | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/r2d4-rellm/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: rellm - **Adopt for:** rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library. ## 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, mit license, distilled models, commercial use. - 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 rellm if… - Tags unique to rellm: llm, huggingface-transformers, transformers. - - When you require precise and exact structure in output data generated from any language model, utilizing rellm can ensure consistency. - Leaner open-issue backlog (5). ## 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 rellm - - Avoid using rellm if you are not working with the Hugging Face Transformers library or do not need structured output formats. - - If your project can tolerate some level of unstructured or less rigidly formatted outputs from language models, other solutions might be more appropriate. ## Common questions ### What is the difference between DeepSeek-R1 and rellm? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. rellm: Exact structure out of any language model completion. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over rellm? Choose DeepSeek-R1 over rellm 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, mit license, distilled models, commercial use; 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 rellm over DeepSeek-R1? Choose rellm over DeepSeek-R1 when Tags unique to rellm: llm, huggingface-transformers, transformers; - When you require precise and exact structure in output data generated from any language model, utilizing rellm can ensure consistency; Leaner open-issue backlog (5). ### 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 rellm? - Avoid using rellm if you are not working with the Hugging Face Transformers library or do not need structured output formats. - If your project can tolerate some level of unstructured or less rigidly formatted outputs from language models, other solutions might be more appropriate. ### Is DeepSeek-R1 or rellm more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 513). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and rellm open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, rellm: MIT). ### Where can I find alternatives to DeepSeek-R1 or rellm? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [rellm alternatives](/tools/r2d4-rellm/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [rellm markdown twin](/tools/r2d4-rellm/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-r2d4-rellm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or rellm? DeepSeek-R1: Dormant. rellm: 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 rellm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [rellm trust report](/tools/r2d4-rellm/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/_