--- title: "Auto-PyTorch vs DeepSeek-R1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-deepseek-ai-deepseek-r1" tools: ["automl-auto-pytorch", "deepseek-ai-deepseek-r1"] --- # Auto-PyTorch vs DeepSeek-R1 *GraphCanon updated Jul 12, 2026* ## Verdict Pick Auto-PyTorch when license: Auto-PyTorch is Apache-2.0, DeepSeek-R1 is MIT; pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, Auto-PyTorch is Apache-2.0. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 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 [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | | Stars | 2,539 | 91,991 | | Forks | 303 | 11,711 | | Open issues | 75 | 45 | | Language | Python | - | | Adopt for | - | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Days since push | 823d | 379d | | Open issues (now) | 75 | 45 | | Security scan | 40 low (40 low) | No lockfile | | Full report | [trust report](/tools/automl-auto-pytorch/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 Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, DeepSeek-R1 is MIT. - Tags unique to Auto-PyTorch: automl, deep-learning, python, pytorch. - Auto-PyTorch ships Docker support for self-hosted deployment. ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, Auto-PyTorch 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. - 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 NOT to use Auto-PyTorch - Last GitHub push was 824 days ago (dormant maintenance, Apr 9, 2024). Validate activity before betting a new project on Auto-PyTorch. - 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 Auto-PyTorch and DeepSeek-R1? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. 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 Auto-PyTorch over DeepSeek-R1? Choose Auto-PyTorch over DeepSeek-R1 when License: Auto-PyTorch is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to Auto-PyTorch: automl, deep-learning, python, pytorch; Auto-PyTorch ships Docker support for self-hosted deployment. ### When should I choose DeepSeek-R1 over Auto-PyTorch? Choose DeepSeek-R1 over Auto-PyTorch when License: DeepSeek-R1 is MIT, Auto-PyTorch 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; 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 avoid Auto-PyTorch? Last GitHub push was 824 days ago (dormant maintenance, Apr 9, 2024). Validate activity before betting a new project on Auto-PyTorch. 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 Auto-PyTorch or DeepSeek-R1 more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 2,539). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and DeepSeek-R1 open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, DeepSeek-R1: MIT). ### Where can I find alternatives to Auto-PyTorch or DeepSeek-R1? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/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/automl-auto-pytorch-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, Auto-PyTorch or DeepSeek-R1? Auto-PyTorch: 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 Auto-PyTorch and DeepSeek-R1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-pytorch`](/api/graphcanon/graph?tool=automl-auto-pytorch) - 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/_