--- title: "Awesome-AIGC-Tutorials vs NExT-GPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/luban-agi-awesome-aigc-tutorials-vs-next-gpt-next-gpt" tools: ["luban-agi-awesome-aigc-tutorials", "next-gpt-next-gpt"] --- # Awesome-AIGC-Tutorials vs NExT-GPT *GraphCanon updated Aug 18, 2026* ## Verdict Pick Awesome-AIGC-Tutorials if awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry; pick NExT-GPT if nExT-GPT is focused on multimodal capabilities and instruction tuning for a large language model, targeting researchers and developers interested in multimodal applications. [Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) reports 4.5k GitHub stars, 303 forks, and 10 open issues, last pushed Mar 31, 2024. [NExT-GPT](https://next-gpt.github.io/) has 3.6k stars, 359 forks, and 81 open issues, last pushed May 13, 2025. Figures are from public GitHub metadata via [Awesome-AIGC-Tutorials's repository](https://github.com/luban-agi/Awesome-AIGC-Tutorials) and [NExT-GPT's repository](https://github.com/NExT-GPT/NExT-GPT). | | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | [NExT-GPT](/tools/next-gpt-next-gpt.md) | | --- | --- | --- | | Tagline | Curated tutorials and resources for Large Language Models, AI Painting, and more | Code and models for ICML 2024 paper on multimodal large language model | | Stars | 4,522 | 3,637 | | Forks | 303 | 359 | | Open issues | 10 | 81 | | Language | - | Python | | Adopt for | Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. | NExT-GPT is focused on multimodal capabilities and instruction tuning for a large language model, targeting researchers and developers interested in multimodal applications. | | Persona | - | - | | Runtime | - | - | | License | MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. | BSD-3-Clause | | Categories | Developer Tools, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | [NExT-GPT](/tools/next-gpt-next-gpt.md) | | --- | --- | --- | | Days since push | 848d | 461d | | Open issues (now) | 10 | 81 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/luban-agi-awesome-aigc-tutorials/trust.md) | [trust report](/tools/next-gpt-next-gpt/trust.md) | ## Decision facts: Awesome-AIGC-Tutorials - **Requirements:** No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial. - **Adopt for:** Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. - **License detail:** MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. ## Decision facts: NExT-GPT - **Pricing:** freemium - NExT-GPT is open-source under BSD-3-Clause license, indicating a free but restricted-for-commercial-use model without associated direct monetary cost. - **Requirements:** Min 8 GB RAM; - The repository notes that the code and models are intended for non-commercial use only and must not be used in any illegal or harmful contexts.; - Potential commercial users should seek approval from the authors, making it unsuitable without prior authorization if commercial application is considered. - **Adopt for:** NExT-GPT is focused on multimodal capabilities and instruction tuning for a large language model, targeting researchers and developers interested in multimodal applications. ## Choose when ### Choose Awesome-AIGC-Tutorials if… - License: Awesome-AIGC-Tutorials is MIT, NExT-GPT is BSD-3-Clause. - Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.. - Tags unique to Awesome-AIGC-Tutorials: ai, aigc, deep-learning, midjourney. - Also covers Developer Tools. - If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources. ### Choose NExT-GPT if… - License: NExT-GPT is BSD-3-Clause, Awesome-AIGC-Tutorials is MIT. - Pricing: NExT-GPT is open-source under BSD-3-Clause license, indicating a free but restricted-for-commercial-use model without associated direct monetary cost.. - Requirements: Min 8 GB RAM; - The repository notes that the code and models are intended for non-commercial use only and must not be used in any illegal or harmful contexts.; - Potential commercial users should seek approval from the authors, making it unsuitable without prior authorization if commercial application is considered.. - Tags unique to NExT-GPT: foundation-models, instruction-tuning, large language models, visual-language-learning. - - If you are conducting research specifically centered around multimodal interactions (combining text with visual elements) aligning with the scope of NExT-GPT. ## When NOT to use Awesome-AIGC-Tutorials - Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. - Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis. ## When NOT to use NExT-GPT - - When your project necessitates a production-ready solution, as NExT-GPT is positioned purely for research and non-commercial use. - - If your application requires the model to be used in contexts like illegal, harmful, violent, racist, or sexual purposes, since its usage guidelines explicitly prohibit such applications. ## Common questions ### What is the difference between Awesome-AIGC-Tutorials and NExT-GPT? Awesome-AIGC-Tutorials: Curated tutorials and resources for Large Language Models, AI Painting, and more. NExT-GPT: Code and models for ICML 2024 paper on multimodal large language model. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AIGC-Tutorials over NExT-GPT? Choose Awesome-AIGC-Tutorials over NExT-GPT when License: Awesome-AIGC-Tutorials is MIT, NExT-GPT is BSD-3-Clause; Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.; Tags unique to Awesome-AIGC-Tutorials: ai, aigc, deep-learning, midjourney; Also covers Developer Tools; If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources. ### When should I choose NExT-GPT over Awesome-AIGC-Tutorials? Choose NExT-GPT over Awesome-AIGC-Tutorials when License: NExT-GPT is BSD-3-Clause, Awesome-AIGC-Tutorials is MIT; Pricing: NExT-GPT is open-source under BSD-3-Clause license, indicating a free but restricted-for-commercial-use model without associated direct monetary cost.; Requirements: Min 8 GB RAM; - The repository notes that the code and models are intended for non-commercial use only and must not be used in any illegal or harmful contexts.; - Potential commercial users should seek approval from the authors, making it unsuitable without prior authorization if commercial application is considered.; Tags unique to NExT-GPT: foundation-models, instruction-tuning, large language models, visual-language-learning; - If you are conducting research specifically centered around multimodal interactions (combining text with visual elements) aligning with the scope of NExT-GPT. ### When should I avoid Awesome-AIGC-Tutorials? Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis. ### When should I avoid NExT-GPT? - When your project necessitates a production-ready solution, as NExT-GPT is positioned purely for research and non-commercial use. - If your application requires the model to be used in contexts like illegal, harmful, violent, racist, or sexual purposes, since its usage guidelines explicitly prohibit such applications. ### Is Awesome-AIGC-Tutorials or NExT-GPT more popular on GitHub? Awesome-AIGC-Tutorials has more GitHub stars (4,522 vs 3,637). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AIGC-Tutorials and NExT-GPT open source? Yes - both are open-source projects on GitHub (Awesome-AIGC-Tutorials: MIT, NExT-GPT: BSD-3-Clause). ### Where can I find alternatives to Awesome-AIGC-Tutorials or NExT-GPT? GraphCanon lists graph-backed alternatives at [Awesome-AIGC-Tutorials alternatives](/tools/luban-agi-awesome-aigc-tutorials/alternatives) and [NExT-GPT alternatives](/tools/next-gpt-next-gpt/alternatives) ([Awesome-AIGC-Tutorials markdown twin](/tools/luban-agi-awesome-aigc-tutorials/alternatives.md), [NExT-GPT markdown twin](/tools/next-gpt-next-gpt/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/luban-agi-awesome-aigc-tutorials-vs-next-gpt-next-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AIGC-Tutorials or NExT-GPT? Awesome-AIGC-Tutorials: Dormant. NExT-GPT: 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 Awesome-AIGC-Tutorials and NExT-GPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AIGC-Tutorials trust report](/tools/luban-agi-awesome-aigc-tutorials/trust); [NExT-GPT trust report](/tools/next-gpt-next-gpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=luban-agi-awesome-aigc-tutorials`](/api/graphcanon/graph?tool=luban-agi-awesome-aigc-tutorials) - 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/_