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Comparison

open-r1 vs MiniMax-M1

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 MiniMax-M1 if consider MiniMax-M1 for its unparalleled hybrid-attention framework and robust deployment options with vLLM or direct use via Transformers.

Markdown twin · open-r1 alternatives · MiniMax-M1 alternatives

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open-r1 logo

open-r1

huggingface/open-r1

★ 26kpushed Apr 2, 2026
vs
MiniMax-M1 logo

MiniMax-M1

MiniMax-AI/MiniMax-M1

★ 3.2kpushed Jul 7, 2025

Trust & integrity

Signalopen-r1MiniMax-M1
Maintenance
Slowing (100d since push)
As of today · github_public_v1
Dormant (368d 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 reportMiniMax-M1 trust reportTrust methodology

Tagline

open-r1
Fully open reproduction of DeepSeek-R1
MiniMax-M1
MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.

Stars

open-r1
26k
MiniMax-M1
3.2k

Forks

open-r1
2.4k
MiniMax-M1
284

Open issues

open-r1
340
MiniMax-M1
31

Language

open-r1
Python
MiniMax-M1
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.
MiniMax-M1
Consider MiniMax-M1 for its unparalleled hybrid-attention framework and robust deployment options with vLLM or direct use via Transformers.

Persona

open-r1
-
MiniMax-M1
-

Runtime

open-r1
-
MiniMax-M1
-

License

open-r1
The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
MiniMax-M1
MiniMax-M1 is available under the Apache-2.0 license, allowing developers to both modify and use it in both commercial and non-commercial projects with minimal restrictions.

Last pushed

open-r1
Apr 2, 2026
MiniMax-M1
Jul 7, 2025

Categories

open-r1
Model Training, Inference & Serving
MiniMax-M1
LLM Frameworks, Model Training, Inference & Serving

Trust and health

Maintenance

open-r1
Slowing (36%)
MiniMax-M1
Dormant (18%)

Days since push

open-r1
100d
MiniMax-M1
368d

Open issues (now)

open-r1
340
MiniMax-M1
31

Full report

open-r1
Trust report
MiniMax-M1
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: deepseek-r1, rl pipeline, vllm, cuda.
  • 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 MiniMax-M1 if…

  • Downloadable from HuggingFace repository: [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k) and [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k)
  • Pricing: While the model is accessible under an open-source license, deploying MiniMax-M1 may involve costs related to computational resources..
  • Requirements: Min 64 GB RAM; Requires Docker; Efficient deployment using vLLM or Transformers; significant RAM and processing power required due to its nature as a large-scale model..
  • Tags unique to MiniMax-M1: llm, large-language-models, reasoning-models, minimax-m1.
  • Also covers LLM Frameworks.
  • - When you require a large-scale model offering high performance in complex reasoning tasks, especially where efficient memory management is critical.

When NOT to use MiniMax-M1

  • - Avoid using MiniMax-M1 if the primary requirement is a lightweight solution due to limited computational resources, as its large-scale nature demands significant memory and processing power.
  • - Do not use if real-time performance is absolutely critical since vLLM while efficient, might introduce additional latency compared to more optimized private or proprietary solutions tailored for low
GitHubDetails

Explore

open-r1 trust report →MiniMax-M1 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 · MiniMax-M1 3.2k (synced Jul 12, 2026).

Common questions

What is the difference between open-r1 and MiniMax-M1?
open-r1: Fully open reproduction of DeepSeek-R1. MiniMax-M1: MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.. See the comparison table for live GitHub stats and shared categories.
When should I choose open-r1 over MiniMax-M1?
Choose open-r1 over MiniMax-M1 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, cuda; 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 MiniMax-M1 over open-r1?
Choose MiniMax-M1 over open-r1 when Downloadable from HuggingFace repository: MiniMax-M1-40k and MiniMax-M1-80k; Pricing: While the model is accessible under an open-source license, deploying MiniMax-M1 may involve costs related to computational resources.; Requirements: Min 64 GB RAM; Requires Docker; Efficient deployment using vLLM or Transformers; significant RAM and processing power required due to its nature as a large-scale model.; Tags unique to MiniMax-M1: llm, large-language-models, reasoning-models, minimax-m1; Also covers LLM Frameworks; - When you require a large-scale model offering high performance in complex reasoning tasks, especially where efficient memory management is critical.
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 MiniMax-M1?
- Avoid using MiniMax-M1 if the primary requirement is a lightweight solution due to limited computational resources, as its large-scale nature demands significant memory and processing power. - Do not use if real-time performance is absolutely critical since vLLM while efficient, might introduce additional latency compared to more optimized private or proprietary solutions tailored for low
Is open-r1 or MiniMax-M1 more popular on GitHub?
open-r1 has more GitHub stars (26,401 vs 3,159). Stars measure visibility, not whether either tool fits your constraints.
Are open-r1 and MiniMax-M1 open source?
Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, MiniMax-M1: Apache-2.0).
Where can I find alternatives to open-r1 or MiniMax-M1?
GraphCanon lists graph-backed alternatives at open-r1 alternatives and MiniMax-M1 alternatives (open-r1 markdown twin, MiniMax-M1 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 MiniMax-M1?
open-r1: Slowing. MiniMax-M1: Dormant. 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 MiniMax-M1?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; MiniMax-M1 trust report.