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蚂蚁开源Ling-3.0-flash-VL：124B参数原生多模态模型，支持视觉反馈闭环
蚂蚁开源团队正式发布并开源Ling-3.0-flash-VL，这是百灵系列首个原生多模态大模型。该模型基于Ling-3.0-flash的MoE架构，总参数124B，每次推理激活5.5B参数，原生支持图像、文本和视频输入，上下文窗口达256K Tokens。模型引入视觉反馈机制，通过观察执行结果、对比目标、识别偏差并持续修正，将传统一次性生成转变为具备自我修正能力的动态过程，提升最终结果的可靠性。同时，该模型继承了Ling-3.0-flash作为Agent工作流高效执行节点的优势，在视觉反馈闭环中平衡输出质量与执行效率。
AI推荐理由 蚂蚁开源团队正式发布Ling-3.0-flash-VL，124B参数MoE架构，原生多模态，支持图像、文本、视频输入，上下文256K，激活参数5.5B。文章介绍了视觉反馈机制和Agent工作流中的效率优势，信息具体，来源为AIbase转载，但内容基于官方发布，可信度较高。对关注多模态模型和Agent应用的开发者有参考价值。
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AntGroup Open Sources Ling-3.0-flash-VL: The 124B-Parameter Native Multimodal Large Model Makes Its Debut
AIbase基地
Published inAI News · 3 min read · Sep 9, 2026
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The Ant Open Source team officially released and open-sourced the first native multimodal large model of the Bailing series, Ling-3.0-flash-VL. This model is based on the MoE architecture of Ling-3.0-flash and has a total parameter count of 124B. It activates 5.5B parameters per inference and natively supports image, text, and video input with a context window of 256K Tokens.
During the development process, the team focused on exploring how to make large models more reliable and efficient in completing real-world tasks. In response to the industry's common concern that "adding visual capabilities to large models will reduce text intelligence," the actual training practices provided the opposite conclusion: native multimodal joint training not only expanded the application boundaries but also further enhanced text intelligence.
To achieve more accurate execution results, Ling-3.0-flash-VL introduces an innovative visual feedback mechanism. Through a complete closed-loop process of "observing the execution result, comparing it with the goal, identifying deviations, and continuously correcting," the model transforms the traditional one-time "generation" into a dynamic process with self-correction capabilities, significantly improving the reliability of the final result.
Additionally, this model inherits the core advantage of Ling-3.0-flash as an efficient execution node in Agent workflows. In the closed-loop operation with visual feedback, it can perfectly balance output quality and execution efficiency, advancing and completing complex tasks with lower costs and shorter time.
AI New TermsBrand Product TermsMultimodal Large ModelVisual Feedback Mechanism
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