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Comparison

open-r1 vs awesome-LLM-resources

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 awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.

Markdown twin · open-r1 alternatives · awesome-LLM-resources alternatives

GraphCanon updated Oct 10, 2026

17views this month

open-r1 logo

open-r1

huggingface/open-r1

★ 26kpushed Oct 2, 2026
vs
awesome-LLM-resources logo

awesome-LLM-resources

WangRongsheng/awesome-LLM-resources

★ 9.0kpushed Sep 14, 2026

Trust & integrity

Signalopen-r1awesome-LLM-resources
Maintenance
Active (8d since push)
As of Oct 10, 2026 · github_public_v1
Very active (3d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Oct 10, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
open-r1 trust reportawesome-LLM-resources trust reportTrust methodology

Tagline

open-r1
Fully open reproduction of DeepSeek-R1
awesome-LLM-resources
Summary of the world's best LLM resources.

Stars

open-r1
26k
awesome-LLM-resources
9.0k

Forks

open-r1
2.5k
awesome-LLM-resources
993

Open issues

open-r1
0
awesome-LLM-resources
40

Language

open-r1
Python
awesome-LLM-resources
-

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.
awesome-LLM-resources
awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.

Persona

open-r1
-
awesome-LLM-resources
-

Runtime

open-r1
-
awesome-LLM-resources
-

License

open-r1
The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
awesome-LLM-resources
The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.

Last pushed

open-r1
Oct 2, 2026
awesome-LLM-resources
Sep 14, 2026

Categories

open-r1
Inference & Serving, Model Training
awesome-LLM-resources
AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training

Trust and health

Maintenance

open-r1
Active (82%)
awesome-LLM-resources
Very active (96%)

Days since push

open-r1
8d
awesome-LLM-resources
3d

Open issues (now)

open-r1
0
awesome-LLM-resources
40

Stars delta

open-r1
+28 (30d)
awesome-LLM-resources
+123 (30d)

Open issues delta

open-r1
-340 (30d)
awesome-LLM-resources
+17 (30d)

Owner type

open-r1
Organization
awesome-LLM-resources
User

Full report

open-r1
Trust report
awesome-LLM-resources
Trust report

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.

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 awesome-LLM-resources if…

  • Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs..
  • Requirements: The repository does not specify any technical requirements for accessing its content..
  • Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models.
  • Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, LLM Frameworks.
  • When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.

When NOT to use awesome-LLM-resources

  • If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider.
  • When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
GitHubDetails

Explore

open-r1 trust report →awesome-LLM-resources trust report →Inference & Serving category →Model Training category →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Evaluation & Observability 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 · awesome-LLM-resources 9.0k (synced Oct 10, 2026).

Common questions

What is the difference between open-r1 and awesome-LLM-resources?
open-r1: Fully open reproduction of DeepSeek-R1. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
When should I choose open-r1 over awesome-LLM-resources?
Choose open-r1 over awesome-LLM-resources 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 awesome-LLM-resources over open-r1?
Choose awesome-LLM-resources over open-r1 when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, LLM Frameworks; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
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 awesome-LLM-resources?
If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
Is open-r1 or awesome-LLM-resources more popular on GitHub?
open-r1 has more GitHub stars (26,477 vs 8,968). Stars measure visibility, not whether either tool fits your constraints.
Are open-r1 and awesome-LLM-resources open source?
Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, awesome-LLM-resources: Apache-2.0).
Where can I find alternatives to open-r1 or awesome-LLM-resources?
GraphCanon lists graph-backed alternatives at open-r1 alternatives and awesome-LLM-resources alternatives (open-r1 markdown twin, awesome-LLM-resources 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 awesome-LLM-resources?
open-r1: Active. awesome-LLM-resources: 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 awesome-LLM-resources?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; awesome-LLM-resources trust report.

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