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Comparison

open-r1 vs litgpt

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 litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment.

Markdown twin · open-r1 alternatives · litgpt alternatives

GraphCanon updated today

open-r1 logo

open-r1

huggingface/open-r1

★ 26kpushed Apr 2, 2026
vs
litgpt logo

litgpt

Lightning-AI/litgpt

★ 13kpushed Jul 6, 2026

Trust & integrity

Signalopen-r1litgpt
Maintenance
Slowing (100d since push)
As of today · github_public_v1
Very active (4d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none
open-r1 trust reportlitgpt trust reportTrust methodology

Tagline

open-r1
Fully open reproduction of DeepSeek-R1
litgpt
High-performance LLMs with recipes for pretraining, finetuning and deployment

Stars

open-r1
26k
litgpt
13k

Forks

open-r1
2.4k
litgpt
1.5k

Open issues

open-r1
340
litgpt
267

Language

open-r1
Python
litgpt
Python

Adopt for

open-r1
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.
litgpt
LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment.

Persona

open-r1
-
litgpt
-

Runtime

open-r1
-
litgpt
-

License

open-r1
The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
litgpt
LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification.

Last pushed

open-r1
Apr 2, 2026
litgpt
Jul 6, 2026

Categories

open-r1
Model Training, Inference & Serving
litgpt
LLM Frameworks, Model Training, Inference & Serving

Trust and health

Maintenance

open-r1
Slowing (36%)
litgpt
Very active (96%)

Days since push

open-r1
100d
litgpt
4d

Open issues (now)

open-r1
340
litgpt
267

Full report

open-r1
Trust report
litgpt
Trust report

Shared compatibility

  • Python · open-r1: Python runtime · litgpt: Python runtime

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: deepseek-r1, rl pipeline, vllm, python.
  • Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.

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.
GitHubDetails

Choose litgpt if…

  • Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models..
  • Requirements: Min 16 GB RAM.
  • Tags unique to litgpt: llms, deep-learning, ai, artificial-intelligence.
  • Also covers LLM Frameworks.
  • If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application.

When NOT to use litgpt

  • If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources.
  • When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable.
GitHubDetails

Explore

open-r1 trust report →litgpt trust report →Model Training category →Inference & Serving category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: open-r1 26k · litgpt 13k (synced Jul 12, 2026).

Common questions

What is the difference between open-r1 and litgpt?
open-r1: Fully open reproduction of DeepSeek-R1. litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. See the comparison table for live GitHub stats and shared categories.
When should I choose open-r1 over litgpt?
Choose open-r1 over litgpt 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: deepseek-r1, rl pipeline, vllm, python; 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 litgpt over open-r1?
Choose litgpt over open-r1 when Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.; Requirements: Min 16 GB RAM; Tags unique to litgpt: llms, deep-learning, ai, artificial-intelligence; Also covers LLM Frameworks; If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application.
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 litgpt?
If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable.
Is open-r1 or litgpt more popular on GitHub?
open-r1 has more GitHub stars (26,401 vs 13,473). Stars measure visibility, not whether either tool fits your constraints.
Are open-r1 and litgpt open source?
Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, litgpt: Apache-2.0).
Where can I find alternatives to open-r1 or litgpt?
GraphCanon lists graph-backed alternatives at open-r1 alternatives and litgpt alternatives (open-r1 markdown twin, litgpt 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, open-r1 or litgpt?
open-r1: Slowing. litgpt: Very 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 litgpt?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; litgpt trust report.