--- title: "gpt-neox vs NExT-GPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-next-gpt-next-gpt" tools: ["eleutherai-gpt-neox", "next-gpt-next-gpt"] --- # gpt-neox vs NExT-GPT *GraphCanon updated Aug 18, 2026* ## Verdict Pick gpt-neox if gPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license; 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. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. [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 [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [NExT-GPT's repository](https://github.com/NExT-GPT/NExT-GPT). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [NExT-GPT](/tools/next-gpt-next-gpt.md) | | --- | --- | --- | | Tagline | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | Code and models for ICML 2024 paper on multimodal large language model | | Stars | 7,452 | 3,637 | | Forks | 1,119 | 359 | | Open issues | 111 | 81 | | Language | Python | Python | | Adopt for | GPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license. | 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 | The tool is licensed under Apache-2.0, allowing permissive use but emphasizing that derivative works must preserve copyright headers and licenses as per their origins | BSD-3-Clause | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [NExT-GPT](/tools/next-gpt-next-gpt.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 56d | 461d | | Open issues (now) | 111 | 81 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/next-gpt-next-gpt/trust.md) | ## Decision facts: gpt-neox - **Pricing:** freemium - Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations. - **Adopt for:** GPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license. - **License detail:** The tool is licensed under Apache-2.0, allowing permissive use but emphasizing that derivative works must preserve copyright headers and licenses as per their origins ## 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 gpt-neox if… - License: gpt-neox is Apache-2.0, NExT-GPT is BSD-3-Clause. - Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations.. - Tags unique to gpt-neox: deepspeed-library, gpt-3, language-model, transformers. - - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters. ### Choose NExT-GPT if… - License: NExT-GPT is BSD-3-Clause, gpt-neox is Apache-2.0. - 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: chatgpt, foundation-models, instruction-tuning, large language models. - - 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 gpt-neox - - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure. - - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties. ## 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 gpt-neox and NExT-GPT? gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. 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 gpt-neox over NExT-GPT? Choose gpt-neox over NExT-GPT when License: gpt-neox is Apache-2.0, NExT-GPT is BSD-3-Clause; Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations.; Tags unique to gpt-neox: deepspeed-library, gpt-3, language-model, transformers; - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters. ### When should I choose NExT-GPT over gpt-neox? Choose NExT-GPT over gpt-neox when License: NExT-GPT is BSD-3-Clause, gpt-neox is Apache-2.0; 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: chatgpt, foundation-models, instruction-tuning, large language models; - 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 gpt-neox? - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure. - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties. ### 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 gpt-neox or NExT-GPT more popular on GitHub? gpt-neox has more GitHub stars (7,452 vs 3,637). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-neox and NExT-GPT open source? Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, NExT-GPT: BSD-3-Clause). ### Where can I find alternatives to gpt-neox or NExT-GPT? GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [NExT-GPT alternatives](/tools/next-gpt-next-gpt/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/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/eleutherai-gpt-neox-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, gpt-neox or NExT-GPT? gpt-neox: Steady. 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 gpt-neox and NExT-GPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [NExT-GPT trust report](/tools/next-gpt-next-gpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eleutherai-gpt-neox`](/api/graphcanon/graph?tool=eleutherai-gpt-neox) - 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/_