--- title: "MiniMax-01 vs MiniMax-M1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minimax-ai-minimax-01-vs-minimax-ai-minimax-m1" tools: ["minimax-ai-minimax-01", "minimax-ai-minimax-m1"] --- # MiniMax-01 vs MiniMax-M1 Neutral, constraint-first comparison with live GitHub stats. | | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | [MiniMax-M1](/tools/minimax-ai-minimax-m1.md) | | --- | --- | --- | | 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 | 3,444 | 3,159 | | Forks | 330 | 283 | | Open issues | 8 | 31 | | 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. | | Categories | LLM Frameworks, Computer Vision | Model Training, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | [MiniMax-M1](/tools/minimax-ai-minimax-m1.md) | | --- | --- | --- | | Open issues (now) | 8 | 31 | | Full report | [trust report](/tools/minimax-ai-minimax-01/trust.md) | [trust report](/tools/minimax-ai-minimax-m1/trust.md) | **Typed relationship:** MiniMax-01 _(successor)_ MiniMax-M1 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. Coexists - While MiniMax-M1 may be older, both models can coexist in different applications depending on specific needs. ## Decision facts: MiniMax-01 - **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 ## Decision facts: MiniMax-M1 - **Requirements:** Min 8 GB RAM; Requires Python environment - **Adopt for:** 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, - **License detail:** The repository is available under the Apache License 2.0, allowing users to utilize it freely while providing attribution as specified in the license. ## Choose when ### 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 ### 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-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 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. ## 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 (3,444 vs 3,159). 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. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=minimax-ai-minimax-01`](/api/graphcanon/graph?tool=minimax-ai-minimax-01) - 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/_