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I built an open-source tool that explores multiple AI models in parallel instead of betting on one

The problem I kept hitting: every image model has different strengths, but I never know which one fits a given brief until I’ve already spent money testing them. So I built Image Lab 1.0 . You give it a creative brief. It classifies what kind of image you’re trying to create, works out which models are a good fit, shows you the estimated cost, and — once you approve — generates several alternatives across those models in parallel. You see the actual cost, then compare the

五维评分

付费意愿
38
需求真实度
58
个人可落地
82
竞争空间
42
新鲜度
72

AI 分析

痛点:AI 图像模型各有所长,创作者无法预知哪个模型适合当前 brief,只能先花钱试错才能看到结果,缺乏生成前的成本预估与多模型横向对比。

目标用户:高频使用 AI 图像生成的营销/设计团队、独立创作者、广告与电商代理机构。

证据:every image model has different strengths, but I never know which one fits a given brief until I’ve already spent money testing them. / 24 images total · $1.0554 all-in / The actual cost was shown before every run

变现:免费开源工具引流,商业化做托管 SaaS:按生成量或模型路由抽成、团队协作与预算控制付费层、为品牌/代理提供成本优化报告。

MVP:2 周 MVP:输入 creative brief → 用 LLM 分类意图并路由到 2-3 个主流图像模型 → 生成前显示预估成本 → 并行出图 → 画廊对比选择并导出选定方向。

竞品:Fal.ai、Replicate、PromptPerfect、OpenRouter(文本模型路由)

摘要:作者发布开源工具 Image Lab 1.0,解决“该用哪个 AI 图像模型”的痛点:输入 brief 后自动分类、匹配模型、显示预估成本并并行生成多方案对比。实测一个 campaign brief 三轮迭代共产 24 张图,总成本 1.0554 美元。工具本身免费开源,尚无他人付费证据,但核心痛点真实且可低成本复刻。