--- title: "open-r1 vs openpi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-physical-intelligence-openpi" tools: ["huggingface-open-r1", "physical-intelligence-openpi"] --- # open-r1 vs openpi *GraphCanon updated Jul 12, 2026* ## Verdict Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; pick openpi if openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations. [open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 2026. [openpi](https://github.com/Physical-Intelligence/openpi) has 13k stars, 2.2k forks, and 312 open issues, last pushed Jun 16, 2026. Figures are from public GitHub metadata via [open-r1's repository](https://github.com/huggingface/open-r1) and [openpi's repository](https://github.com/Physical-Intelligence/openpi). | | [open-r1](/tools/huggingface-open-r1.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Tagline | Fully open reproduction of DeepSeek-R1 | Repository for running AI models with GPU requirements specified. | | Stars | 26,401 | 12,742 | | Forks | 2,446 | 2,187 | | Open issues | 340 | 312 | | Language | Python | Python | | Adopt for | Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. | openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations. | | Persona | - | - | | Runtime | - | - | | License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-r1](/tools/huggingface-open-r1.md) | [openpi](/tools/physical-intelligence-openpi.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 100d | 25d | | Open issues (now) | 340 | 312 | | Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/physical-intelligence-openpi/trust.md) | ## Decision facts: open-r1 - **Requirements:** Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical. - **Adopt for:** Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. - **License detail:** The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. ## Decision facts: openpi - **Adopt for:** openpi is a specialized tool for model training, inference & serving that leverages advanced GPU capabilities and has specific requirements for memory and hardware configurations. ## Choose when ### Choose open-r1 if… - Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.. - Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation. - Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### Choose openpi if… - Tags unique to openpi: fine-tuning, lora, model parallelism, nvidia gpu. - When you have an NVIDIA GPU with at least 8 GB of VRAM for inference or at least 22.5 GB to fine-tune models using LoRA (Low-Rank Adaptation) on a single GPU. - More recently updated (last pushed Jun 16, 2026). ## When NOT to use open-r1 - Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. - Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ## When NOT to use openpi - When your project is not compatible with Ubuntu 22.04 or if you do not have access to a supported GPU configuration. - If you need to support multi-node training, as this capability has yet to be implemented in the current version of openpi. ## Common questions ### What is the difference between open-r1 and openpi? open-r1: Fully open reproduction of DeepSeek-R1. openpi: Repository for running AI models with GPU requirements specified.. See the comparison table for live GitHub stats and shared categories. ### When should I choose open-r1 over openpi? Choose open-r1 over openpi when Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### When should I choose openpi over open-r1? Choose openpi over open-r1 when Tags unique to openpi: fine-tuning, lora, model parallelism, nvidia gpu; When you have an NVIDIA GPU with at least 8 GB of VRAM for inference or at least 22.5 GB to fine-tune models using LoRA (Low-Rank Adaptation) on a single GPU; More recently updated (last pushed Jun 16, 2026). ### When should I avoid open-r1? Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ### When should I avoid openpi? When your project is not compatible with Ubuntu 22.04 or if you do not have access to a supported GPU configuration. If you need to support multi-node training, as this capability has yet to be implemented in the current version of openpi. ### Is open-r1 or openpi more popular on GitHub? open-r1 has more GitHub stars (26,401 vs 12,742). Stars measure visibility, not whether either tool fits your constraints. ### Are open-r1 and openpi open source? Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, openpi: Apache-2.0). ### Where can I find alternatives to open-r1 or openpi? GraphCanon lists graph-backed alternatives at [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) and [openpi alternatives](/tools/physical-intelligence-openpi/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/alternatives.md), [openpi markdown twin](/tools/physical-intelligence-openpi/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/huggingface-open-r1-vs-physical-intelligence-openpi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, open-r1 or openpi? open-r1: Slowing. openpi: Active. 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 open-r1 and openpi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-r1 trust report](/tools/huggingface-open-r1/trust); [openpi trust report](/tools/physical-intelligence-openpi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-open-r1`](/api/graphcanon/graph?tool=huggingface-open-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/_