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Comparison

open-r1 vs aikit

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 aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies.

Markdown twin · open-r1 alternatives · aikit alternatives

GraphCanon updated Sep 20, 2026

15views this month

open-r1 logo

open-r1

huggingface/open-r1

★ 26kpushed Apr 2, 2026
vs
aikit logo

aikit

kaito-project/aikit

★ 539pushed Sep 18, 2026

Trust & integrity

Signalopen-r1aikit
Maintenance
Slowing (156d since push)
As of Sep 6, 2026 · github_public_v1
Very active (0d since push)
As of Sep 19, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 6, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 19, 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 Jul 11, 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 reportaikit trust reportTrust methodology

Tagline

open-r1
Fully open reproduction of DeepSeek-R1
aikit
Fine-tune, build, and deploy open-source LLMs easily!

Stars

open-r1
26k
aikit
539

Forks

open-r1
2.4k
aikit
57

Open issues

open-r1
340
aikit
37

Language

open-r1
Python
aikit
Go

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.
aikit
Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies.

Persona

open-r1
-
aikit
-

Runtime

open-r1
-
aikit
-

License

open-r1
The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
aikit
MIT

Last pushed

open-r1
Apr 2, 2026
aikit
Sep 18, 2026

Categories

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

Trust and health

Maintenance

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

Days since push

open-r1
156d
aikit
0d

Open issues (now)

open-r1
340
aikit
37

Stars delta

open-r1
+26 (30d)
aikit
+5 (30d)

Open issues delta

open-r1
0 (30d)
aikit
-6 (30d)

Full report

open-r1
Trust report
aikit
Trust report

Choose open-r1 if…

  • open-r1 is primarily Python; aikit is Go.
  • License: open-r1 is Apache-2.0, aikit 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..
  • 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 aikit if…

  • aikit is primarily Go; open-r1 is Python.
  • License: aikit is MIT, open-r1 is Apache-2.0.
  • Tags unique to aikit: ai, buildkit, chatgpt, docker.
  • Also covers LLM Frameworks.
  • aikit ships Docker support for self-hosted deployment.
  • - You need a flexible solution specifically built using Go and prefer its concurrency model.

When NOT to use aikit

  • - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit.
  • - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
GitHubDetails

Explore

open-r1 trust report →aikit trust report →Inference & Serving category →Model Training 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 · aikit 539 (synced Sep 20, 2026).

Common questions

What is the difference between open-r1 and aikit?
open-r1: Fully open reproduction of DeepSeek-R1. aikit: Fine-tune, build, and deploy open-source LLMs easily!. See the comparison table for live GitHub stats and shared categories.
When should I choose open-r1 over aikit?
Choose open-r1 over aikit when open-r1 is primarily Python; aikit is Go; License: open-r1 is Apache-2.0, aikit 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.; 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 aikit over open-r1?
Choose aikit over open-r1 when aikit is primarily Go; open-r1 is Python; License: aikit is MIT, open-r1 is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model.
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 aikit?
- You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
Is open-r1 or aikit more popular on GitHub?
open-r1 has more GitHub stars (26,449 vs 539). Stars measure visibility, not whether either tool fits your constraints.
Are open-r1 and aikit open source?
Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, aikit: MIT).
Where can I find alternatives to open-r1 or aikit?
GraphCanon lists graph-backed alternatives at open-r1 alternatives and aikit alternatives (open-r1 markdown twin, aikit 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 aikit?
open-r1: Slowing. aikit: 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 aikit?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; aikit trust report.

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