--- title: "DeepSeek-R1 vs HRM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-sapientinc-hrm" tools: ["deepseek-ai-deepseek-r1", "sapientinc-hrm"] --- # DeepSeek-R1 vs HRM *GraphCanon updated Aug 17, 2026* ## Verdict Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick HRM if hierarchical Reasoning Model (HRM) is a brain-inspired AI tool centered on deep learning and hierarchical reasoning. It necessitates CUDA 12.6 for its PyTorch-based environment setup, making it uniquely optimized for GPU. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 38 open issues, last pushed Jun 27, 2025. [HRM](https://sapient.inc) has 13k stars, 1.8k forks, and 75 open issues, last pushed Mar 31, 2026. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [HRM's repository](https://github.com/sapientinc/HRM). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [HRM](/tools/sapientinc-hrm.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Hierarchical Reasoning Model Official Release | | Stars | 91,982 | 12,613 | | Forks | 11,706 | 1,825 | | Open issues | 38 | 75 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | Hierarchical Reasoning Model (HRM) is a brain-inspired AI tool centered on deep learning and hierarchical reasoning. It necessitates CUDA 12.6 for its PyTorch-based environment setup, making it uniquely optimized for GPU | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | 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) | [HRM](/tools/sapientinc-hrm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 405d | 138d | | Open issues (now) | 38 | 75 | | Stars delta | Unknown | +17 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/sapientinc-hrm/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: HRM - **Adopt for:** Hierarchical Reasoning Model (HRM) is a brain-inspired AI tool centered on deep learning and hierarchical reasoning. It necessitates CUDA 12.6 for its PyTorch-based environment setup, making it uniquely optimized for GPU ## Choose when ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, HRM 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. - 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 HRM if… - License: HRM is Apache-2.0, DeepSeek-R1 is MIT. - Tags unique to HRM: brain-inspired-ai, deep-learning, large language models, reasoning. - Consider HRM when you need to leverage a highly specific GPU version (CUDA 12.6) which can potentially offer the latest in computational capabilities tailored for deep learning tasks. ## 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 HRM - Avoid using HRM if you face limitations or challenges in accessing CUDA 12.6 specifically, as the model is tightly coupled with this version of CUDA and other versions will not be compatible. - Do not use HRM if your project does not benefit from hierarchical reasoning models; its specialized architecture could represent an unnecessary complexity. ## Common questions ### What is the difference between DeepSeek-R1 and HRM? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. HRM: Hierarchical Reasoning Model Official Release. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over HRM? Choose DeepSeek-R1 over HRM when License: DeepSeek-R1 is MIT, HRM 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; 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 HRM over DeepSeek-R1? Choose HRM over DeepSeek-R1 when License: HRM is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to HRM: brain-inspired-ai, deep-learning, large language models, reasoning; Consider HRM when you need to leverage a highly specific GPU version (CUDA 12.6) which can potentially offer the latest in computational capabilities tailored for deep learning tasks. ### 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 HRM? Avoid using HRM if you face limitations or challenges in accessing CUDA 12.6 specifically, as the model is tightly coupled with this version of CUDA and other versions will not be compatible. Do not use HRM if your project does not benefit from hierarchical reasoning models; its specialized architecture could represent an unnecessary complexity. ### Is DeepSeek-R1 or HRM more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,982 vs 12,613). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and HRM open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, HRM: Apache-2.0). ### Where can I find alternatives to DeepSeek-R1 or HRM? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [HRM alternatives](/tools/sapientinc-hrm/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [HRM markdown twin](/tools/sapientinc-hrm/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-sapientinc-hrm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or HRM? DeepSeek-R1: Dormant. HRM: 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 HRM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [HRM trust report](/tools/sapientinc-hrm/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/_