蚂蚁百灵发布新一代原生混合推理模型Ling-3.0-flash，总参数量124B，激活参数量仅5.1B，在传统推理、指令遵循与长文本等指标上对标甚至超越上一代旗舰Ring-2.6-1T。
模型采用原生混合线性注意力架构与1/64稀疏MoE，并扩展至10，000+可交互训练环境，长输入下TTFT降低60%至80%以上。
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Ant Bailing releases Ling-3.0-flash native hybrid inference model
Ant Group's BaLing has released the new generation native hybrid reasoning model Ling-3.0-flash, with a total parameter count of 124B and only 5.1B active...
Today AI Intelligence Brief
Ant Group's BaLing has released the new generation native hybrid reasoning model Ling-3.0-flash,
with a total parameter count of 124B and only 5.1B active parameters. In traditional reasoning, instruction following, and long-text benchmarks, it matches or even surpasses the previous flagship Ring-2.6-1T. The model adopts a native hybrid linear attention architecture with 1/64 sparse MoE and has been expanded to over 10,000 interactive training environments, reducing TTFT under long inputs by more than 60% to 80%.
资讯正文 · AI 整理
Intelligence Assessment
Aioga 编辑摘要
蚂蚁百灵发布原生混合推理模型Ling-3.0-flash，总参数量124B、激活参数量5.1B，并称其在传统推理、指令遵循和长文本等指标上对标甚至超越Ring-2.6-1T。
背景分析
公开材料显示，该模型采用原生混合线性注意力架构与1/64稀疏MoE，训练范围扩展至超过10,000个可交互环境；在长输入场景下，TTFT降低60%至80%以上。
Aioga 观点
Aioga 判断，Ling-3.0-flash的重点是以较低激活参数量兼顾推理、指令遵循和长文本表现，但现有材料未提供具体评测集、测试条件及第三方验证结果。
影响与后续
Aioga 判断，混合线性注意力与稀疏MoE可能有助于改善长输入处理效率；不过，官方披露的指标能否稳定复现，以及不同任务下的实际表现，仍值得关注。 后续可重点关注完整技术报告、评测方法、基准成绩与测试环境，并核验TTFT降幅的对照模型、输入长度及部署条件，以判断相关性能表述的适用范围。
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Source: 公众号：蚂蚁百灵（Ling）
Original link: Open original source
Aioga archive: Open intelligence page
Content record: summary-fallback · Updated: 2026-07-24T13:40:30.000Z
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