Vercel 数据:Jev 成为其采用最快的 AI 模型
Jev is Vercel’s fastest-adopted AI model ever
Vercel 周四公布的数据显示,Typesafe AI 的决策模型 Jev 上线两周内被 35% 的付费团队使用,覆盖范围接近第二受欢迎模型 Sonnet 5.5 的两倍,成为 Vercel 平台上首两周采用最快的模型。Jev 于 9 月 15 日限量发布,定位为在少量预设输出间做选择的轻量工具,可更便宜快速地完成大规模数据分类。数据还显示,开源模型已占 Vercel 网关处理 token 的 70% 以上,但仅占总支出的近 25%。
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Jev has become the fastest adopted AI model ever — at least by some measures.
Vercel data published on Thursday shows that, within two weeks of its launch, the buzzy decision model was being used by 35% of paid teams, with nearly double the reach of the second most popular model, Sonnet 5.5. It’s the fastest adoption a model has ever seen in its first two weeks, according to Vercel’s data, and a major accomplishment for the model’s creators, Typesafe AI.
Published in limited release on September 15, Jev is designed as a lightweight tool for choosing between a few pre-determined outputs. Limiting the outputs lets the model run cheaper and faster than alternatives like Sonnet, making it ideal for classifying large quantities of data quickly. That new approach has attracted significant buzz in the weeks since Jev’s release — inspiring competing offerings from OpenAI and Amazon , among others — but until now there has been little hard data to back up the good vibes.
The data comes from the Vercel’s AI gateway , a unified API service similar to OpenRouter. It’s a necessarily limited view — particularly at risk of undercounting major labs like OpenAI and Anthropic that offer their own APIs — but it shows the buzz around Jev really did translate into actual usage. In fact, Vercel was an early adopter of Jev, using it to replace GPT-6 Luna for classifying harmful commands.
The report also shows continuing momentum for open-source models, which now account for more than 70% of the tokens processed through Vercel’s gateway and nearly 25% of the total spend.
While open-source models are capturing a larger share of the market, the market is also growing fast. Spending on frontier labs has grown ten-fold, even as it accounts for a smaller share of overall spend, giving credence to the idea that frontier models and open-source alternatives may turn out to be complementary rather than competitive .