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AI 导读
Rayline 发布小型 AI 模型 ARC，用于为智能体任务的每一步选择由哪个模型处理以及需要多少思考量。用户把任务交给 ARC 后，简单步骤会被分配给更轻量的模型、困难步骤交给更强的模型，以提升速度并降低成本。在 Rayline 的 DeepSWE 测试中，ARC 得分 68.7%，Opus 5.5 为 69.9%，而 ARC 将每个任务的成本降至 7%。
推荐理由
ARC 用路由模型按步骤分配模型与思考量，原文给出与 Opus 5.5 的对比分数和成本比例，可据此判断路由方案的实际取舍。
正文 · 原文
Rayline has launched ARC, a tiny AI model designed to pick which model should handle each step of a task and how much it should think.
Users can send a task to ARC and have it automatically assign easy steps to a lighter model and hard ones to a stronger model, for a faster, more cost-efficient experience.
In Rayline's DeepSWE tests, ARC scored 68.7% versus 69.9% for Opus 5.5, while dropping the cost to 7% per task.
In 2015, I built an AI that detects early cancer. Today, we’re introducing ARC by Rayline: the world’s first unified model for routing AI. > 97.5% of Opus 5.5 quality > Up to 1/10th of the cost > Fully open-source It picks the right model and the right amount of thinking for every step of your agent’s work. Try it here and read our technical report below: http://rayline.ai在 X 查看被引用的帖子
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