---
title: "open-r1 vs MiniMax-M1"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-minimax-ai-minimax-m1"
tools: ["huggingface-open-r1", "minimax-ai-minimax-m1"]
---

# open-r1 vs MiniMax-M1

*GraphCanon updated Jul 12, 2026*

## 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.

[open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 2026. [MiniMax-M1](https://www.minimax.io/) has 3.2k stars, 284 forks, and 31 open issues, last pushed Jul 7, 2025. Figures are from public GitHub metadata via [open-r1's repository](https://github.com/huggingface/open-r1) and [MiniMax-M1's repository](https://github.com/MiniMax-AI/MiniMax-M1).

| | [open-r1](/tools/huggingface-open-r1.md) | [MiniMax-M1](/tools/minimax-ai-minimax-m1.md) |
| --- | --- | --- |
| Tagline | Fully open reproduction of DeepSeek-R1 | MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. |
| Stars | 26,401 | 3,159 |
| Forks | 2,446 | 284 |
| Open issues | 340 | 31 |
| Language | Python | Python |
| Adopt for | 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. | Consider MiniMax-M1 for its unparalleled hybrid-attention framework and robust deployment options with vLLM or direct use via Transformers. |
| Persona | - | - |
| Runtime | - | - |
| License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | 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. |
| Categories | Model Training, Inference & Serving | Model Training, LLM Frameworks, Inference & Serving |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [open-r1](/tools/huggingface-open-r1.md) | [MiniMax-M1](/tools/minimax-ai-minimax-m1.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 100d | 368d |
| Open issues (now) | 340 | 31 |
| Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/minimax-ai-minimax-m1/trust.md) |

## Decision facts: open-r1

- **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.
- **Adopt for:** 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.
- **License detail:** The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.

## Decision facts: MiniMax-M1

- **Hosting:** harness plugin - 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:** commercial - 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.
- **Adopt for:** Consider MiniMax-M1 for its unparalleled hybrid-attention framework and robust deployment options with vLLM or direct use via Transformers.
- **License detail:** 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.

## Choose when

### 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.

### 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 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 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

## 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](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 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](/tools/huggingface-open-r1/alternatives) and [MiniMax-M1 alternatives](/tools/minimax-ai-minimax-m1/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/alternatives.md), [MiniMax-M1 markdown twin](/tools/minimax-ai-minimax-m1/alternatives.md)), 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](/compare/huggingface-open-r1-vs-minimax-ai-minimax-m1.md) 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](/tools/huggingface-open-r1/trust); [MiniMax-M1 trust report](/tools/minimax-ai-minimax-m1/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=huggingface-open-r1`](/api/graphcanon/graph?tool=huggingface-open-r1)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_