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LatestMeituanFlash-Omni
MeituanAny-to-Any

Meituan Debuts LongCat-Flash-Omni, an Any-to-Any AI Model

The new open-source Mixture-of-Experts model can process and generate any combination of text, images, video, audio, and 3D data.

Oct 23, 2025
NotableMIT
LongCat-Flash-Omni

Chinese technology company Meituan has released LongCat-Flash-Omni, a new open-source model designed to handle a vast array of data types. Billed as an "any-to-any" omni-modal system, it aims to process and generate information across different formats seamlessly, a significant step in developing more versatile AI.

According to the release details on Hugging Face, the model's key innovation is its ability to manage multiple data modalities simultaneously within a single framework. This "omni-modal" capability includes:

  • Text
  • Images
  • Video
  • Audio
  • 3D data

The model is built on a Mixture-of-Experts (MoE) architecture, a technique that activates specialized parts of the network for specific tasks. This allows the model to scale efficiently while managing the complexity of different data encoders. The system projects inputs from various modalities into a shared representational space, allowing the core language model to process them cohesively.

Released under the permissive MIT license, LongCat-Flash-Omni provides the research community with a powerful tool for exploring truly multimodal AI. Its flexible architecture opens up possibilities for complex applications that require understanding and generating content across diverse formats, pushing the boundaries of what's possible with open-source models.

Sources

  • meituan-longcat/LongCat-Flash-Omni

    Hugging Face

    Visit
OlderNVIDIA Releases Real-Time Speaker Diarization ModelNVIDIA · Speech → Text · last yearNewerMeituan Releases Open-Source LongCat-Video ModelMeituan · Text → Video · last year

Get the model

Hugging Face

Specs

Context window131K tokens
Size1.1 TB
PrecisionBF16
ArchitectureLongcatFlashOmniForCausalLM
LicenseMIT
Downloads417
Likes116

Can you run it?

Datacenter only — about 337.9 GB even at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    1.1 TB
  • 8-bit

    8×H100 80 GB node

    595.9 GB
  • 4-bit

    8×H100 80 GB node

    337.9 GB
Your machine
  • Mixture-of-experts: all 561B parameters must be resident even though only 27B are active per token — active count affects speed, not memory.
  • 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-Any

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