LongCat-Next

LongCat-Next

Native multimodal model that handles text, images and audio as discrete tokens in one system, with 74.3B parameters. For researchers and developers building apps that see, listen and generate.

Made by meituan-longcat on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: LongCat-Next is a native multimodal model that handles text, images and audio in one system. It turns pictures and sound into discrete tokens, so a single next-token objective covers every kind of input. It has 74.3 billion parameters, about 3 billion of them active, and the team released its tokenizers too.

Who it suits: It suits researchers and developers who want one open model that can look at images, listen to audio and generate in return.

Industry | Who uses it | How they use it | Example
Research | Multimodal researchers | Study unified discrete token training | Comparing vision understanding and generation in one model
Software | App developers | Build apps that see, listen and speak | A helper that answers questions about a photo
Media | Content teams | Prototype tools that mix text, image and audio | Drafting captions for a clip

You can skip it if you only need a text model, since this one is built for several input types.

Where to use it: It is meant for servers with enough GPU memory. The download step works on Windows, macOS or Linux with Python.

• A GPU server for serving the weights
• Research code that uses the released tokenizers
• Windows, macOS or Linux, for fetching the files

Use case | Who | What they get back
Image understanding | Developers | Answers about what a picture shows
Image generation | Creators | New images from the same model
Audio tasks | Voice app builders | Spoken input and output through the same system

How to set it up: You need Python and a large amount of disk. Hugging Face lists about 149 GB in BF16, or an estimated 37.1 GB at 4-bit. The card links to a technical report and a demo for more.

• Python and the Hugging Face tool
• About 149 GB of disk for the BF16 weights
• A serving setup that can handle a 74B model

1. Install the Hugging Face download tool.
2. Download the repository files to a drive with enough room.
3. Follow the model card to run the model and its tokenizers.

pip install -U huggingface_hub
hf download meituan-longcat/LongCat-Next

How to use it: After the setup, feed the model text, images or audio and read its replies. The same model covers seeing, creating and talking. The visible part of the card gives no run command, so follow its own instructions.

Tool | What it does
hf download | Fetches the model files to your computer

Example: You show the model a photo and ask what is in it. It reads the image as tokens and writes an answer in text. In the same system you could also give it speech as input.

For newcomers and experts: If you are new, this is a research-grade model that sees, listens and talks, meant for people with server hardware. Begin by reading the model card and the technical report to see how it is used.

If you know the field, the idea is a discrete native autoregressive paradigm. Vision and audio become tokens in a shared embedding space beside text, on top of the LongCat-Flash-Lite mixture-of-experts backbone. The authors say this lifts discrete vision modelling on understanding tasks and unifies understanding with generation.

Text, image or audio input
Tokenizers make discrete tokens
One mixture-of-experts model
Text, image or audio output

Good to know: The card describes it as a research release and shares its tokenizers for further work.

• License: MIT
• Cost to run: no API key, account or paid service is named, but you need GPU hardware for a 74B model
• Data it reaches: files downloaded from Hugging Face, plus the images, audio and text you give it
• Not for: small machines, or use where you need exact benchmark numbers the card does not show here

On Hugging Face, 13 November 2026

• Task: any to any
• Library: LongCat-Next
• Parameters: 74.3B
• Weights: about 149 GB in BF16; about 37.1 GB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 561
• Likes: 220
• License: mit
• Quantized versions: 5
• Fine-tunes: 1
• Tags: longcat-next, safetensors, longcat_next, text-generation, transformers, multimodal, any-to-any

Links

• Model: https://huggingface.co/meituan-longcat/LongCat-Next
• Files: https://huggingface.co/meituan-longcat/LongCat-Next/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download meituan-longcat/LongCat-Next

Then follow the usage in its model card. Nothing in this kit runs by itself.