按表格顺序，依次是Glm-4.1v-thinking-flash,GLM-Z1 airx ，GLM-Z1-flash,Deepseek-v3-0324,GPT-4.1nano.
评审员：Deepseek-R1-0528
评比标准：你现在是ai翻译质量测评专家，接下来我给你一段原文，和若干ai翻译，你评测一下翻译质量如何，主要侧重于对原文语义的传达和是否贴合中文表达习惯，通顺流利度以及专业术语翻译的准确度上考量。
结果：glm-Z1-flash 遥遥领先（当然这个结果主观性很强，值得一提的是，这块模型参数量只有9B）
之所以用z1-flash还是出于速度上的考量，输出速度几乎跟v3无异，而且官方完全免费，支持高并发.
原文： The Recip function in the Softmax module exhibits a high dynamic range in our statistic experiments. To accurately sample the function results on the sheer range between 0 to 1, the table needs to be very large. Initially, storing the reciprocal function would have needed an entire BRAM bank (depth=1024, width=36) to maintain accuracy. To minimize BRAM usage, we exploited the function’s inherent properties and segmented it into two parts, each owning an independent scaling factor. We empirically divide the input range at the first 1/8 for the steep part and the remainder for the flat. This approach is visualized in Figure 10d. The orange line is the abs error of the original LUT implementation of Recip, and the blue one is the segmented implementation with the segmentation pivot annotated to it. With more entries between 0 and 1, the sampling is more accurate, reducing Mean Squared Error (MSE) from 0.032 to 0.0034.
冠军模型译文：
Softmax 模块中的 Recip 函数在我们的统计实验中表现出高动态范围。为精确采样 0 至 1 区间内的函数结果，需要构建庞大的查找表（LUT）。最初直接存储倒数函数需占用整个 BRAM 存储体（深度=1024，位宽=36）以确保精度。为最小化 BRAM 消耗，我们利用函数固有特性将其划分为两部分，各配独立缩放因子。经实验将输入范围首 1/8 划分为陡峭部分，余量作为平坦部分（图 10d）。橙色曲线为原始 LUT 实现 Recip 的绝对误差，蓝色曲线为分段实现，标注了分段临界点。通过在 0-1 区间增加采样点数，采样精度显著提升，使均方误差（MSE）从 0.032 降至 0.0034。
第二次测评：
