--- title: "Hands-On-Large-Language-Models vs MiniMax-01" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-minimax-ai-minimax-01" tools: ["handsonllm-hands-on-large-language-models", "minimax-ai-minimax-01"] --- # Hands-On-Large-Language-Models vs MiniMax-01 *GraphCanon updated Aug 18, 2026* ## Verdict Pick Hands-On-Large-Language-Models if consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples; pick MiniMax-01 if miniMax-01 optimizes Linear Attention for large-language and vision-language models. [Hands-On-Large-Language-Models](https://www.llm-book.com/) reports 28k GitHub stars, 6.5k forks, and 38 open issues, last pushed Apr 24, 2026. [MiniMax-01](https://www.minimax.io/) has 3.5k stars, 332 forks, and 8 open issues, last pushed Jul 7, 2025. Figures are from public GitHub metadata via [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) and [MiniMax-01's repository](https://github.com/MiniMax-AI/MiniMax-01). | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | | --- | --- | --- | | Tagline | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention | | Stars | 28,252 | 3,463 | | Forks | 6,531 | 332 | | Open issues | 38 | 8 | | Language | Jupyter Notebook | Python | | Adopt for | Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. | MiniMax-01 optimizes Linear Attention for large-language and vision-language models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 114d | 406d | | Open issues (now) | 38 | 8 | | Stars delta | +642 (30d) | +17 (30d) | | Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/minimax-ai-minimax-01/trust.md) | **Typed relationship:** Hands-On-Large-Language-Models _(related)_ MiniMax-01 MiniMax-01 is relevant to the practical application and study of large language models, which aligns with the educational content covered in Hands-On Large Language Models. ## Decision facts: Hands-On-Large-Language-Models - **Pricing:** freemium - The repository is free and open under the Apache-2.0 license. - **Requirements:** - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial. - **Adopt for:** Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. - **License detail:** Apache-2.0 License ## Decision facts: MiniMax-01 - **Adopt for:** MiniMax-01 optimizes Linear Attention for large-language and vision-language models. ## Choose when ### Choose Hands-On-Large-Language-Models if… - Hands-On-Large-Language-Models is primarily Jupyter Notebook; MiniMax-01 is Python. - License: Hands-On-Large-Language-Models is Apache-2.0, MiniMax-01 is MIT. - Pricing: The repository is free and open under the Apache-2.0 license.. - Requirements: - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.. - MiniMax-01 is relevant to the practical application and study of large language models, which aligns with the educational content covered in Hands-On Large Language Models. - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llms, oreilly. - - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book. ### Choose MiniMax-01 if… - MiniMax-01 is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook. - License: MiniMax-01 is MIT, Hands-On-Large-Language-Models is Apache-2.0. - MiniMax-01 is relevant to the practical application and study of large language models, which aligns with the educational content covered in Hands-On Large Language Models. - Tags unique to MiniMax-01: vision-language-model, vlm. - When high throughput performance is required for model serving ## When NOT to use Hands-On-Large-Language-Models - - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book. - - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs. ## When NOT to use MiniMax-01 - If deep customization of attention mechanisms aside from Linear Attention is needed - In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility ## Common questions ### What is the difference between Hands-On-Large-Language-Models and MiniMax-01? Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. MiniMax-01: Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hands-On-Large-Language-Models over MiniMax-01? Choose Hands-On-Large-Language-Models over MiniMax-01 when Hands-On-Large-Language-Models is primarily Jupyter Notebook; MiniMax-01 is Python; License: Hands-On-Large-Language-Models is Apache-2.0, MiniMax-01 is MIT; Pricing: The repository is free and open under the Apache-2.0 license.; Requirements: - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.; MiniMax-01 is relevant to the practical application and study of large language models, which aligns with the educational content covered in Hands-On Large Language Models; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llms, oreilly; - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book. ### When should I choose MiniMax-01 over Hands-On-Large-Language-Models? Choose MiniMax-01 over Hands-On-Large-Language-Models when MiniMax-01 is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook; License: MiniMax-01 is MIT, Hands-On-Large-Language-Models is Apache-2.0; MiniMax-01 is relevant to the practical application and study of large language models, which aligns with the educational content covered in Hands-On Large Language Models; Tags unique to MiniMax-01: vision-language-model, vlm; When high throughput performance is required for model serving. ### When should I avoid Hands-On-Large-Language-Models? - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book. - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs. ### When should I avoid MiniMax-01? If deep customization of attention mechanisms aside from Linear Attention is needed In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility ### Is Hands-On-Large-Language-Models or MiniMax-01 more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (28,252 vs 3,463). Stars measure visibility, not whether either tool fits your constraints. ### Are Hands-On-Large-Language-Models and MiniMax-01 open source? Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, MiniMax-01: MIT). ### Where can I find alternatives to Hands-On-Large-Language-Models or MiniMax-01? GraphCanon lists graph-backed alternatives at [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) and [MiniMax-01 alternatives](/tools/minimax-ai-minimax-01/alternatives) ([Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/alternatives.md), [MiniMax-01 markdown twin](/tools/minimax-ai-minimax-01/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/handsonllm-hands-on-large-language-models-vs-minimax-ai-minimax-01.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Hands-On-Large-Language-Models or MiniMax-01? Hands-On-Large-Language-Models: Slowing. MiniMax-01: 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 Hands-On-Large-Language-Models and MiniMax-01? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust); [MiniMax-01 trust report](/tools/minimax-ai-minimax-01/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models) - 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/_