Meituan Unveils LongCat-Next: A Deep Unified Multimodal AI Model Shifting AI Foundations

When the industry is still debating “stitch‑based” multimodal models, Meituan quietly introduced LongCat-Next, a large model that aims to erase the gap between text, image, and speech at a fundamental level, suggesting a profound shift in AI’s underlying logic.

1. Same‑Origin Token: Dismantling the AI “Babel Tower”

LongCat-Next’s core breakthrough is its claim to convert images, speech, and text into the same origin token. The idea is that every modality is encoded into a unified symbol set, allowing the model to operate in a single semantic space from the first layer rather than stitching separate representations together.

What is a “Same‑Origin Token”?

It can be viewed as a “universal basic particle” of AI: whether it is a sunset, a melody, or a line of poetry, the model encodes them into elements of the same symbol system. This mirrors human perception, where sensory inputs are instantly fused into a coherent experience rather than processed separately and later combined.

If validated, this approach could greatly boost AI’s accuracy in complex scenes—for example, autonomous driving that simultaneously processes road visuals, rain‑noise audio, and navigation voice commands, or e‑commerce platforms that seamlessly combine product images, video demos, and spoken customer inquiries.

2. Meituan’s Ambition: From Life Services to AI Infrastructure

Meituan’s motivation stems from its “eat‑sleep‑play‑shop‑entertain” ecosystem, which generates highly multimodal data: dish photos, store videos, voice orders, and massive textual reviews. An insider noted, “Future life services will require AI to deeply understand and proactively satisfy users’ unspoken needs, demanding vision, hearing, and contextual comprehension like a human.”

Consequently, LongCat-Next is expected to first reshape internal systems such as search, recommendation, customer service, and even autonomous delivery, enabling AI to simultaneously verify dish images, interpret vocal complaints, and comprehend historical text feedback.

3. Multimodal Fusion: The Next Battle in AI Evolution

The emergence of LongCat-Next shifts the multimodal competition from “whether” to “how deep” the integration is. The industry consensus is that shallow modality stitching has hit a ceiling, and true intelligence emergence depends on deeper unified representations.

However, significant challenges remain: designing efficient unified encoders, training such massive models, and preventing cross‑modal interference. Meituan has not disclosed financing details or deeper technical specifics, leaving some uncertainty about its actual capabilities.

Overall, Meituan’s move signals that leading tech firms are no longer satisfied with application‑layer innovations and are now targeting AI’s “root technologies.” This is not merely a model upgrade but a paradigm exploration of how AI can understand the world, potentially ushering in a new era where machines interact with humans and the environment in a more human‑like manner.