--- title: "MGM vs JOOD" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jia-lab-research-mgm-vs-naver-ai-jood" tools: ["jia-lab-research-mgm", "naver-ai-jood"] --- # MGM vs JOOD *GraphCanon updated Aug 18, 2026* ## Verdict Pick MGM if mGM offers a focused approach on multi-modal vision-language generation tasks with specific dependency requirements; pick JOOD if jOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. [MGM](https://github.com/JIA-Lab-research/MGM) reports 3.3k GitHub stars, 276 forks, and 61 open issues, last pushed May 4, 2024. [JOOD](https://github.com/naver-ai/JOOD) has 21 stars, 4 forks, and 2 open issues, last pushed Jun 11, 2025. Figures are from public GitHub metadata via [MGM's repository](https://github.com/JIA-Lab-research/MGM) and [JOOD's repository](https://github.com/naver-ai/JOOD). | | [MGM](/tools/jia-lab-research-mgm.md) | [JOOD](/tools/naver-ai-jood.md) | | --- | --- | --- | | Tagline | Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models | Implementation for multimodal LLM jailbreaking strategy | | Stars | 3,331 | 21 | | Forks | 276 | 4 | | Open issues | 61 | 2 | | Language | Python | Python | | Adopt for | MGM offers a focused approach on multi-modal vision-language generation tasks with specific dependency requirements. | JOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MGM](/tools/jia-lab-research-mgm.md) | [JOOD](/tools/naver-ai-jood.md) | | --- | --- | --- | | Days since push | 835d | 419d | | Open issues (now) | 61 | 2 | | Stars delta | +1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/jia-lab-research-mgm/trust.md) | [trust report](/tools/naver-ai-jood/trust.md) | ## Shared compatibility - **Python**: [MGM](/tools/jia-lab-research-mgm.md) - Python runtime; [JOOD](/tools/naver-ai-jood.md) - Python runtime ## Decision facts: MGM - **Adopt for:** MGM offers a focused approach on multi-modal vision-language generation tasks with specific dependency requirements. ## Decision facts: JOOD - **Requirements:** Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository. - **Adopt for:** JOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. ## Choose when ### Choose MGM if… - Tags unique to MGM: additional-packages-training-cases, generation, large language models, multi-modality. - Also covers LLM Frameworks. - When working on projects requiring integration of text and visual data for generation tasks. ### Choose JOOD if… - Requirements: Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository.. - Tags unique to JOOD: jailbreaking, multimodal-llms. - Also covers Computer Vision. - Use JOOD when you need to explore how a multimodal model behaves with unforeseen or out-of-distribution inputs, thus pushing the boundaries of its conventional responses or outputs. ## When NOT to use MGM - Avoid if your project requires commercial licensing, as MGM is strictly research-use only under CC BY NC 4.0. - Not suitable if you are unable to update or ensure the availability of required Python packages like flash-attn and ninja for training purposes. ## When NOT to use JOOD - Avoid using JOOD if jailbreaking strategies are not of interest, such as in scenarios requiring strict adherence to model limitations and ethical constraints. - JOOD may not be suitable if you require tools that focus on improving performance or stability of models rather than exploring unconventional behavior or vulnerabilities. ## Common questions ### What is the difference between MGM and JOOD? MGM: Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models. JOOD: Implementation for multimodal LLM jailbreaking strategy. See the comparison table for live GitHub stats and shared categories. ### When should I choose MGM over JOOD? Choose MGM over JOOD when Tags unique to MGM: additional-packages-training-cases, generation, large language models, multi-modality; Also covers LLM Frameworks; When working on projects requiring integration of text and visual data for generation tasks. ### When should I choose JOOD over MGM? Choose JOOD over MGM when Requirements: Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository.; Tags unique to JOOD: jailbreaking, multimodal-llms; Also covers Computer Vision; Use JOOD when you need to explore how a multimodal model behaves with unforeseen or out-of-distribution inputs, thus pushing the boundaries of its conventional responses or outputs. ### When should I avoid MGM? Avoid if your project requires commercial licensing, as MGM is strictly research-use only under CC BY NC 4.0. Not suitable if you are unable to update or ensure the availability of required Python packages like flash-attn and ninja for training purposes. ### When should I avoid JOOD? Avoid using JOOD if jailbreaking strategies are not of interest, such as in scenarios requiring strict adherence to model limitations and ethical constraints. JOOD may not be suitable if you require tools that focus on improving performance or stability of models rather than exploring unconventional behavior or vulnerabilities. ### Is MGM or JOOD more popular on GitHub? MGM has more GitHub stars (3,331 vs 21). Stars measure visibility, not whether either tool fits your constraints. ### Are MGM and JOOD open source? Yes - both are open-source projects on GitHub (MGM: Apache-2.0, JOOD: Apache-2.0). ### Where can I find alternatives to MGM or JOOD? GraphCanon lists graph-backed alternatives at [MGM alternatives](/tools/jia-lab-research-mgm/alternatives) and [JOOD alternatives](/tools/naver-ai-jood/alternatives) ([MGM markdown twin](/tools/jia-lab-research-mgm/alternatives.md), [JOOD markdown twin](/tools/naver-ai-jood/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/jia-lab-research-mgm-vs-naver-ai-jood.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MGM or JOOD? MGM: Dormant. JOOD: 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 MGM and JOOD? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MGM trust report](/tools/jia-lab-research-mgm/trust); [JOOD trust report](/tools/naver-ai-jood/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jia-lab-research-mgm`](/api/graphcanon/graph?tool=jia-lab-research-mgm) - 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/_