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LatestMeituanNext
MeituanAny-to-Any

Meituan Releases LongCat-Next 'Any-to-Any' AI Model

The Chinese tech company has released the weights for a unified model that can process and generate combinations of text, images, audio, and video.

Mar 25, 2026
NotableMIT
LongCat-Next

Chinese technology company Meituan has released the weights for LongCat-Next, an ambitious 'any-to-any' multimodal model. Published under a permissive MIT license, the model marks a significant step towards more flexible and generalized AI systems that can operate across a wide spectrum of data types.

Unlike most multimodal models that handle specific input-output pairs, such as text-to-image or image-to-text, LongCat-Next is designed for true combinatorial flexibility. It can accept any mix of text, images, audio, and video as input and generate any combination of those modalities as output. For example, it could take an image and an audio clip as prompts and produce a descriptive paragraph and a short video in response.

A Unified Architecture

The model achieves this versatility through a unified framework. Instead of stitching together separate, specialized encoders and decoders for each data type, LongCat-Next uses a single, end-to-end trained network. This architecture relies on a shared vocabulary to represent and process information from different sources, enabling it to generate coherent, multimodal content from complex prompts.

The release of LongCat-Next on the Hugging Face Hub provides researchers and developers with a powerful tool for exploring the frontiers of multimodal AI. Its open-ended capabilities and permissive license encourage experimentation in creative content generation, data synthesis, and complex reasoning tasks that span multiple domains.

Sources

  • meituan-longcat/LongCat-Next

    Hugging Face

    Visit
OlderCohere Releases Top-Ranked Multilingual Transcription ModelCohere · Speech → Text · 7 months agoNewerHKUST Releases Audio-Omni, a Unified Audio ModelHKUSTAudio · Any-to-Any · 7 months ago

Get the model

Hugging Face

Specs

Context window131K tokens
Size150.8 GB
PrecisionBF16
ArchitectureLongcatNextForCausalLM
LicenseMIT
Downloads434
Likes213

Can you run it?

Needs a workstation GPU (48 GB) at 4-bit.

  • BF16 (as published)

    2×H100 80 GB · 256 GB Mac Studio

    151.9 GB
  • 8-bit

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    79.9 GB
  • 4-bit

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    45.7 GB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-AnyText / LLM

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