--- title: "LongCat-Next" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/69f00f61-cad4-4e6e-b6a5-d35699539162" updated: "2026-11-12" --- # LongCat-Next > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. LongCat-Next is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by meituan-longcat ## 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. ```bash 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 ## README ````markdown # 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. ```table 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 ```table 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. ```bash 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. ```table 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. ```flow 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [meituan-longcat/LongCat-Next on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Next) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | LongCat-Next (this page) | 561 | 220 | 74.3B | mit | 2026-11-12 | | [MiniCPM-o 4.5](https://mindket.com/p/9b50c9c1-60cb-4f69-af93-dacbf9f14243) | 170,793 | 1,571 | 9.4B | apache-2.0 | 2026-11-09 | | [Realtime-Venus](https://mindket.com/p/45d3544c-f19f-439a-9ca0-cf4fef04dee6) | 2,101 | 135 | — | apache-2.0 | 2026-10-26 | | [gemma-4-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | apache-2.0 | 2026-11-03 | | [gemma-4-E2B-it](https://mindket.com/p/52ee89d2-d2e7-4750-b100-90e089c3ec76) | 3,017,059 | 1,129 | 5.1B | apache-2.0 | 2026-11-03 | | [gemma-4-12B-it](https://mindket.com/p/6830e0b3-1eaa-4197-9fce-f73b57ec505f) | 1,738,905 | 1,735 | 12B | apache-2.0 | 2026-10-30 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · lcampbell · 2026-06-30](https://news.ycombinator.com/item?id=48728046): LongCat-2.0, a large-scale MoE model with 1.6T total and 48B Active: I don't think llama.cpp supports any of the LongCat models, actually. They haven't posted weights/inference solutions for LongCat-2.0 1, but LongCat-Next had transformers support, which I assume means it works with vLLM/SGLang. Given it's 1.6T, "common hardware" is probably out of the question;… - [Bluesky · Adina Yakup · 2026-03-25](https://bsky.app/profile/adinayakup.bsky.social/post/3mhvvolvnjs2y): LongCat-Next 🐱 A multimodal foundation model released by Meituan huggingface.co/meituan-long... ✨ 74B total - 3B active - MIT ✨ One token space for all modalities ✨ DiNA paradigm for unified learning ✨ Seeing, creating, talking all in one - [Bluesky · Sung Kim · 2026-03-26](https://bsky.app/profile/sungkim.bsky.social/post/3mhwmfii2n22v): 📄 Paper: github.com/meituan-long... 🔗 GitHub: github.com/meituan-long... 😊 HuggingFace: huggingface.co/meituan-long... 💻 demo: longcat.chat/longcat-next 📖 blog: longcat.chat/longcat-next... - [Bluesky · Paper · 2026-04-02](https://bsky.app/profile/paper.bsky.social/post/3mihwdb7lwz2q): 5/30 35 Likes, 4 Comments, 1 Posts 2603.27538, cs․CV \| cs․CL, 29 Mar 2026 🆕LongCat-Next: Lexicalizing Modalities as Discrete Tokens Meituan LongCat Team, Bin Xiao, Chao Wang, Chengjiang Li, Chi Zhang, Chong Peng, Hang Yu, Hao Yang, Haonan Yan, Haoze Sun, Haozhe Zhao, Hong Liu, Hui Su, Jiaq... - [Bluesky · arXiv cs.CV Computer Vision and Pattern Recognition · 2026-03-31](https://bsky.app/profile/cscv-bot.bsky.social/post/3midkkhvme52d): Meituan LongCat Team, et al.: LongCat-Next: Lexicalizing Modalities as Discrete Tokens https://arxiv.org/abs/2603.27538 https://arxiv.org/pdf/2603.27538 https://arxiv.org/html/2603.27538 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-12 - First published: 2026-03-25 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meituan-longcat/LongCat-Next on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Next) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/69f00f61-cad4-4e6e-b6a5-d35699539162) - [Its page on Mindket](https://mindket.com/p/69f00f61-cad4-4e6e-b6a5-d35699539162)