Meituan has officially launched its AI-native browser, Tabbit, into a free public beta test. This new product is positioned as an 'intelligent work partner,' but its strategic importance goes much deeper, representing a crucial move to find a new growth engine.
At its core, Tabbit is Meituan's answer to mounting financial and competitive pressures. The company faced significant losses in late 2025 and issued a profit warning in February 2026, creating an urgent need for new, low-cost ways to attract and retain users. Tabbit is designed to be this new entry point, or 'top-of-funnel' surface, capturing users' attention at the very beginning of their online journey—before they even decide what to buy or where to go.
Several key factors made this launch possible. First, Meituan had the technological foundation. The company developed its own powerful and efficient AI models, the 'LongCat' series. These models are notable for their low inference cost, meaning it's cheap for Meituan to run the AI that powers the browser's smart features. This makes offering a sophisticated AI product for free economically viable.
Second, the competitive landscape demanded action. Rivals like Baidu and Tencent were already integrating their own powerful AI into search and browser products, setting a new standard for user expectations. Meituan needed to launch its own contender to stay relevant in the AI race and prevent users from drifting to competing ecosystems.
Finally, the timing aligns perfectly with Meituan's recent business expansion. The agreement to acquire the fresh-grocery delivery company Dingdong in February 2026 created a massive new inventory of products. Tabbit can act as the perfect bridge, interpreting a user's general search query like 'healthy dinner ideas' and intelligently guiding them toward purchasing groceries from Dingdong through the Meituan platform. In essence, Tabbit is a strategic tool to convert user intent into transactions within its own ecosystem.
- Top-of-funnel: The initial stage in the customer acquisition process, where a company builds awareness and attracts a wide audience of potential customers.
- Inference Cost: The computational cost associated with using a trained AI model to make predictions or generate outputs based on new data.
- AI-native: A product or service designed from the ground up to be centered around artificial intelligence capabilities, rather than having AI features added on later.