Overview
Scenario-based model selection platform for AI product implementation The core problem we want to solve is the gap from "score" to "selection" There is a structural problem in the field of large-scale model evaluation: there is an insurmountable gap between the existing list design and the decision-making information that users really need. The purpose of users using the list is to make selection decisions - to select the most suitable model for a specific product or work scenario. However, the existing list gives a comprehensive score that is divorced from the scenario: After reading a bunch of evaluation lists, the comprehensive score of Model A is 92, and the comprehensive score of Model B is 88. But what you want to do is to generate a marketing copywriting product, which one should you choose? Model A has stronger mathematical capabilities, but your scenario doesn’t require mathematics at all. Model B is half the price and has better creative writing skills, but it’s not obvious from the list. This is the problem we want to solve: allowing you to make “prescribed medicines” based on real scenarios, rather than blindly pursuing “the strongest”.
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面向 AI 产品落地的场景化模型选型平台我们要解决的核心问题从「分数」到「选型」的鸿沟大模型评测领域有一个结构性问题：现有的榜单设计，和用户真正需要的决策信息之间，存在一道无法逾越的鸿沟。用户使用榜单的目的是做选型决策——在具体的产品或工作场景下，选出最合适的模型。但现有榜单给出的，是一个脱离场景的综合分数：看了一堆评测榜单，模型 A 综合分 92，模型 B 综合分 88但你要做的是营销文案生成产品，到底该选哪个？模型 A 的数学能力更强，但你的场景根本不需要数学模型 B 便宜一半，创意写作能力其实更好，但榜单上看不出来这就是我们要解决的问题：让你能基于真实场景做出「对症下药」的选择，而不是盲目追求「最强」。