Ant Ling· @AntLingAGI · X·· 2026-04-30精选AI 评分72
AI 导读
SGLang团队（隶属于LMSYS Org）揭示了其旗舰指令模型实现快速、高效、大规模执行的关键在于可靠的基础设施与针对性优化。团队宣布对AntLingAGI发布的Ling-2.6-1T万亿参数模型提供Day-0支持。该模型采用快速思考方法，在保持质量的同时，成本可比同类模型降低约4倍，并在AIME26和SWE-bench基准测试中达到SOTA水平。它专为高级编码、复杂推理和大规模智能体工作流设计，具备万亿参数能力与即时模型延迟。团队正持续进行优化，以进一步提升性能。
推荐理由
万亿参数做到即时延迟和4倍成本优势，还有SWE-bench SOTA，这份承诺如果兑现，会改变大规模Agent部署的性价比计算。值得去cookbook跑一下验证。
正文 · 原文
What's the secret sauce behind the flagship instruct model built for fast execution & high efficiency at scale? Reliable infra with the proper optimizations, from the #SGLang friends at @lmsysorg
以为昨天的 100B 已经打满，今日 1T 方知，打得还可以更满～ 🥳 Onto the next optimization~ 🫡
👏 Meet Ling-2.6-1T from @AntLingAGI, the trillion-parameter flagship instant instruct model built for fast execution & high efficiency at scale. Day-0 support is now live in SGLang! 1️⃣ Fast thinking approach: ~4x cheaper than comparable models, no quality compromise 2️⃣ SOTA on AIME26 & SWE-bench Verified 3️⃣ Built for advanced coding, complex reasoning & large-scale agent workflows 4️⃣ Trillion-param capability with instant-model latency Cookbook: https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-2.6在 X 查看被引用的帖子
来源：Ant Ling · x.com