Jev is the fastest Worsel AI model ever adopted

The data comes from the Vercel AI Gateway, an integrated API service similar to OpenRouter.

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Jev has become the fastest AI model ever used – at least by some measures.

Within two weeks of launch, the buzzy decision model was being used by 35 percent of paid teams, nearly twice as much as the second most popular model, Sonnet 5.5, Vercel data released Thursday showed. According to Vercel's data, this is the fastest adoption a model has ever seen in its first two weeks, and is a huge achievement for model creators, Typesafe AI.

Released in limited edition on September 15th, Jev is designed as a lightweight tool for choosing between multiple preset outputs. Limiting the outputs allows the model to run cheaper and faster than alternatives such as Sonnet, making it ideal for quickly classifying large amounts of data. This new approach has attracted considerable buzz in the weeks since Jev's release — inspiring competing proposals from OpenAI and Amazon, among others — but so far there's been little hard data to back up the good vibes.

The data comes from the Vercel AI Gateway, an integrated API service similar to OpenRouter. This is a necessarily limited view – especially at the risk of the small number of large labs like OpenAI and Anthropic offering their own APIs – but it shows that the buzz around Jev has really translated into real use. In fact, Versel was an early adopter of Jev, using it to replace Luna's GPT-6 to classify malicious commands.

The report also shows that open source models, which now account for more than 70% of tokens processed through Vercel's gateway and nearly 25% of total spending, continue to gain momentum.

The market is also growing rapidly, while open source models account for a larger share of the market. Costs for frontier labs have increased tenfold, even though they account for a smaller share of overall spending, lending credence to the idea that frontier models and open source alternatives may be complementary rather than competitive.

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