DeepSeek-R1 vs open-r1

Common questions

What is the difference between DeepSeek-R1 and open-r1?

DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. open-r1: Fully open reproduction of DeepSeek-R1. See the comparison table for live GitHub stats and shared categories.

When should I choose DeepSeek-R1 over open-r1?

Choose DeepSeek-R1 over open-r1 when License: DeepSeek-R1 is MIT, open-r1 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.; HuggingFace Open-R1 is a fully open reproduction of DeepSeek-R1, tackling the same problem areas with similar capabilities; 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 choose open-r1 over DeepSeek-R1?

Choose open-r1 over DeepSeek-R1 when License: open-r1 is Apache-2.0, DeepSeek-R1 is MIT; 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.; HuggingFace Open-R1 is a fully open reproduction of DeepSeek-R1, tackling the same problem areas with similar capabilities; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Also covers Inference & Serving; 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 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 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.

Is DeepSeek-R1 or open-r1 more popular on GitHub?

DeepSeek-R1 has more GitHub stars (91,991 vs 26,401). Stars measure visibility, not whether either tool fits your constraints.

Are DeepSeek-R1 and open-r1 open source?

Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, open-r1: Apache-2.0).

Where can I find alternatives to DeepSeek-R1 or open-r1?

GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and open-r1 alternatives (DeepSeek-R1 markdown twin, open-r1 markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, DeepSeek-R1 or open-r1?

DeepSeek-R1: Dormant. open-r1: 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 open-r1?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; open-r1 trust report.