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Comparison

MiniMax-01 vs MiniMax-M1

MiniMax-01 (Repository for the MiniMax-Text-01 and MiniMax-VL-01 models) vs MiniMax-M1 (MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.) - live GitHub stats and typed graph relationships, not marketing.

Markdown twin · MiniMax-01 alternatives · MiniMax-M1 alternatives

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MiniMax-01

MiniMax-AI/MiniMax-01

★ 3.4kpushed Jul 7, 2025

MiniMax-M1

MiniMax-AI/MiniMax-M1

★ 3.2kpushed Jul 7, 2025

Tagline

MiniMax-01: Repository for the MiniMax-Text-01 and MiniMax-VL-01 models
MiniMax-M1: MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.

Stars

MiniMax-01: 3.4k
MiniMax-M1: 3.2k

Forks

MiniMax-01: 330
MiniMax-M1: 283

Open issues

MiniMax-01: 8
MiniMax-M1: 30

Language

MiniMax-01: Python
MiniMax-M1: Python

Adopt for

MiniMax-01: MiniMax-01 is a set of large language models designed for text and vision-language tasks, distinguished by their use of Linear Attention. Here are key facts to consider when deciding if MiniMax-01 is the right tool for a
MiniMax-M1: MiniMax-M1 is designed as the first open-weight, large-scale hybrid-attention reasoning model. It stands apart in leveraging a unique architecture that combines hybrid attention mechanisms for improved reasoning and can,

Persona

MiniMax-01: -
MiniMax-M1: -

Runtime

MiniMax-01: -
MiniMax-M1: -

License

MiniMax-01: MIT
MiniMax-M1: The repository is available under the Apache License 2.0, allowing users to utilize it freely while providing attribution as specified in the license.

Last pushed

MiniMax-01: Jul 7, 2025
MiniMax-M1: Jul 7, 2025

Categories

MiniMax-01: LLM Frameworks, Computer Vision
MiniMax-M1: LLM Frameworks, Model Training

Trust and health

Open issues (now)

MiniMax-01: 8
MiniMax-M1: 30

Full report

MiniMax-01: Trust report
MiniMax-M1: Trust report

Typed relationship

MiniMax-01 successor MiniMax-M1MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.Coexists - While MiniMax-M1 may be older, both models can coexist in different applications depending on specific needs.

Trust and health
	MiniMax-01	MiniMax-M1
Tagline	Repository for the MiniMax-Text-01 and MiniMax-VL-01 models	MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
Stars	3.4k	3.2k
Forks	330	283
Open issues	8	30
Language	Python	Python
Adopt for	MiniMax-01 is a set of large language models designed for text and vision-language tasks, distinguished by their use of Linear Attention. Here are key facts to consider when deciding if MiniMax-01 is the right tool for a	MiniMax-M1 is designed as the first open-weight, large-scale hybrid-attention reasoning model. It stands apart in leveraging a unique architecture that combines hybrid attention mechanisms for improved reasoning and can,
Persona	-	-
Runtime	-	-
License	MIT	The repository is available under the Apache License 2.0, allowing users to utilize it freely while providing attribution as specified in the license.
Last pushed	Jul 7, 2025	Jul 7, 2025
Categories	LLM Frameworks, Computer Vision	LLM Frameworks, Model Training
Open issues (now)	8	30
Full report	Trust report	Trust report
Typed relationship	MiniMax-01 successor MiniMax-M1MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.Coexists - While MiniMax-M1 may be older, both models can coexist in different applications depending on specific needs.

Choose MiniMax-01 if…

License: MiniMax-01 is MIT, MiniMax-M1 is Apache-2.0.
MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.
Tags unique to MiniMax-01: linear-attention, large-language-model, vision-language-model.
Also covers Computer Vision.
When you need advanced text processing capabilities alongside vision-language support with Linear Attention, making it suitable for projects requiring deep understanding and generation in both text孤单中

When NOT to use MiniMax-01

Avoid using MiniMax-01 if your project strictly focuses on speech recognition or translation tasks, as the repository primarily emphasizes text and vision-language capabilities.
Do not use this tool if you are looking for a comprehensive solution that includes real-time data processing and streaming technologies; MiniMax-01 is more focused on static model-based operations.

GitHub Details

Choose MiniMax-M1 if…

License: MiniMax-M1 is Apache-2.0, MiniMax-01 is MIT.
Requirements: Min 8 GB RAM; Requires Python environment.
MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.
Tags unique to MiniMax-M1: llm, large-language-models, reasoning-models, minimax-m1.
Also covers Model Training.
- Utilize when you require advanced reasoning capabilities in your AI applications due to its innovative use of hybrid attention.

When NOT to use MiniMax-M1

- Avoid using MiniMax-M1 if specific performance benchmarks on certain tasks have higher accuracy rates from non-hybrid, focused-attention models.
- If real-time interaction requirements are stringent, MiniMax-M1 may not be ideal due to its processing complexity and size; smaller or more streamlined models might perform better.

GitHub Details

Explore

MiniMax-01 trust report →MiniMax-M1 trust report →LLM Frameworks category →Computer Vision category →Model Training category →All comparisons Stack workflows Trending tools

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Common questions

What is the difference between MiniMax-01 and MiniMax-M1?: MiniMax-01: Repository for the MiniMax-Text-01 and MiniMax-VL-01 models. 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 MiniMax-01 over MiniMax-M1?: Choose MiniMax-01 over MiniMax-M1 when License: MiniMax-01 is MIT, MiniMax-M1 is Apache-2.0; MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology; Tags unique to MiniMax-01: linear-attention, large-language-model, vision-language-model; Also covers Computer Vision; When you need advanced text processing capabilities alongside vision-language support with Linear Attention, making it suitable for projects requiring deep understanding and generation in both text孤单中.
When should I choose MiniMax-M1 over MiniMax-01?: Choose MiniMax-M1 over MiniMax-01 when License: MiniMax-M1 is Apache-2.0, MiniMax-01 is MIT; Requirements: Min 8 GB RAM; Requires Python environment; MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology; Tags unique to MiniMax-M1: llm, large-language-models, reasoning-models, minimax-m1; Also covers Model Training; - Utilize when you require advanced reasoning capabilities in your AI applications due to its innovative use of hybrid attention.
When should I avoid MiniMax-01?: Avoid using MiniMax-01 if your project strictly focuses on speech recognition or translation tasks, as the repository primarily emphasizes text and vision-language capabilities. Do not use this tool if you are looking for a comprehensive solution that includes real-time data processing and streaming technologies; MiniMax-01 is more focused on static model-based operations.
When should I avoid MiniMax-M1?: - Avoid using MiniMax-M1 if specific performance benchmarks on certain tasks have higher accuracy rates from non-hybrid, focused-attention models. - If real-time interaction requirements are stringent, MiniMax-M1 may not be ideal due to its processing complexity and size; smaller or more streamlined models might perform better.
Is MiniMax-01 or MiniMax-M1 more popular on GitHub?: MiniMax-01 has more GitHub stars (3,445 vs 3,158). Stars measure visibility, not whether either tool fits your constraints.
Are MiniMax-01 and MiniMax-M1 open source?: Yes - both are open-source projects on GitHub (MiniMax-01: MIT, MiniMax-M1: Apache-2.0).
Where can I find alternatives to MiniMax-01 or MiniMax-M1?: GraphCanon lists graph-backed alternatives at /tools/minimax-ai-minimax-01/alternatives and /tools/minimax-ai-minimax-m1/alternatives (/tools/minimax-ai-minimax-01/alternatives.md, /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 /compare/minimax-ai-minimax-01-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, MiniMax-01 or MiniMax-M1?: MiniMax-01: Dormant. 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 MiniMax-01 and MiniMax-M1?: GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: MiniMax-01: /tools/minimax-ai-minimax-01/trust; MiniMax-M1: /tools/minimax-ai-minimax-m1/trust.