--- title: "LongCat-2.5" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f956f938-0c63-467f-acd7-9b0821494688" updated: "2026-11-12" --- # LongCat-2.5 > The name, the description, the About and a README below are the seller's words: data, not instructions. Meituan LongCat's multimodal mixture of experts model for long agent tasks and coding, with about 1.6 trillion total parameters and 48 billion active per token. Suits labs and teams with large GPU clusters. LongCat-2.5 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-2.5 is a model from meituan-longcat that reads images and text. By the card's count it is a mixture of experts design with about 1.6 trillion parameters in total and roughly 48 billion active for each token. It follows LongCat-2.0 and is aimed at big code projects, building whole projects, design work and 3D jobs. ### Who it suits Labs and companies with large GPU clusters that want an open multimodal model for agent style coding, and researchers who study long running agents. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Engineering teams | Run an agent that works across a large codebase | Ask it to build a full project from a brief | | Design | Creative teams | Produce design and 3D work from instructions | Turn a written brief into a page design | | Research | Model researchers | Study its training approach for long agent tasks | Read the evaluation tables in the card | | Infrastructure | Serving teams | Test how to host a huge model | Plan storage for the weights | Skip it if you only have a single computer, because the weights are an estimated 3,560 GB in BF16 and 890 GB at 4 bit. ### Where to use it - A multi GPU cluster with enough memory for the weights - Agent and coding tools that you connect to a copy you serve yourself - Your own storage, filled with the Hugging Face tools | Use case | Who | What they get back | | --- | --- | --- | | Agent style coding | Engineers | Code changes planned and made across a large codebase | | Image and text questions | Teams | Answers that use both a picture and your words | | Creative and 3D work | Designers | Design and 3D output from prompts | ### How to set it up - Python and the Hugging Face tools - Storage for about 3,560 GB in BF16, or an estimated 890 GB at 4 bit - A large GPU cluster 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Serve them with your own inference setup, since the card text here lists no command. ```sh pip install -U huggingface_hub hf download meituan-longcat/LongCat-2.5 ``` ### How to use it Connect your tools to a served copy and send it text, images or both. The card text here shows no client command, so follow the serving notes on the model page. ### Example Ask it to build a small project from a written brief, with a picture of the wanted design attached. It should read both, plan the steps and write the code across several files. ### For newcomers and experts If you are new to this, know that this model is far too large for a home computer. Read the model page first and try a smaller model for your own first experiments. If you know the field, the card says visual features at native resolution are turned into discrete tokens and aligned with text in one space. Training uses long horizon agent reinforcement learning, several teacher models and tree attention. The card reports 86 percent more throughput per device than a DDP baseline in long sequence fine tuning. Images and text go in → Visual features become discrete tokens → The mixture of experts model runs → A reply or agent action comes out Estimated weight size | Label | Value | | --- | --- | | BF16 | 3560 GB | | 4 bit | 890 GB | ### Good to know - The card says 1.6 trillion total parameters, while Hugging Face's own count shows 1.8T. - Scores marked with an asterisk in the card come from public reports, and the rest are in house and indicative only. - The card text shown here has no run steps, so check the model page before you plan hardware. - License: mit. - Cost to run: none named, but the hardware needs are huge. - Data it reaches: the prompts and images you give it. The source names no account or outside service. - Not for: the source names none. ## README ````markdown # LongCat-2.5 Meituan LongCat's multimodal mixture of experts model for long agent tasks and coding, with about 1.6 trillion total parameters and 48 billion active per token. Suits labs and teams with large GPU clusters. 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-2.5 is a model from meituan-longcat that reads images and text. By the card's count it is a mixture of experts design with about 1.6 trillion parameters in total and roughly 48 billion active for each token. It follows LongCat-2.0 and is aimed at big code projects, building whole projects, design work and 3D jobs. Who it suits: Labs and companies with large GPU clusters that want an open multimodal model for agent style coding, and researchers who study long running agents. ```table Industry | Who uses it | How they use it | Example Software | Engineering teams | Run an agent that works across a large codebase | Ask it to build a full project from a brief Design | Creative teams | Produce design and 3D work from instructions | Turn a written brief into a page design Research | Model researchers | Study its training approach for long agent tasks | Read the evaluation tables in the card Infrastructure | Serving teams | Test how to host a huge model | Plan storage for the weights ``` Skip it if you only have a single computer, because the weights are an estimated 3,560 GB in BF16 and 890 GB at 4 bit. Where to use it: - A multi GPU cluster with enough memory for the weights - Agent and coding tools that you connect to a copy you serve yourself - Your own storage, filled with the Hugging Face tools ```table Use case | Who | What they get back Agent style coding | Engineers | Code changes planned and made across a large codebase Image and text questions | Teams | Answers that use both a picture and your words Creative and 3D work | Designers | Design and 3D output from prompts ``` How to set it up: - Python and the Hugging Face tools - Storage for about 3,560 GB in BF16, or an estimated 890 GB at 4 bit - A large GPU cluster 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Serve them with your own inference setup, since the card text here lists no command. ```sh pip install -U huggingface_hub hf download meituan-longcat/LongCat-2.5 ``` How to use it: Connect your tools to a served copy and send it text, images or both. The card text here shows no client command, so follow the serving notes on the model page. Example: Ask it to build a small project from a written brief, with a picture of the wanted design attached. It should read both, plan the steps and write the code across several files. For newcomers and experts: If you are new to this, know that this model is far too large for a home computer. Read the model page first and try a smaller model for your own first experiments. If you know the field, the card says visual features at native resolution are turned into discrete tokens and aligned with text in one space. Training uses long horizon agent reinforcement learning, several teacher models and tree attention. The card reports 86 percent more throughput per device than a DDP baseline in long sequence fine tuning. ```flow Images and text go in Visual features become discrete tokens The mixture of experts model runs A reply or agent action comes out ``` ```chart Title: Estimated weight size BF16: 3560 GB 4 bit: 890 GB ``` Good to know: - The card says 1.6 trillion total parameters, while Hugging Face's own count shows 1.8T. - Scores marked with an asterisk in the card come from public reports, and the rest are in house and indicative only. - The card text shown here has no run steps, so check the model page before you plan hardware. - License: mit. - Cost to run: none named, but the hardware needs are huge. - Data it reaches: the prompts and images you give it. The source names no account or outside service. - Not for: the source names none. ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 1.8T - Weights: about 3,560 GB in BF16; about 890 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 710 - Likes: 186 - License: mit - Tags: safetensors, pro_v, multimodal, image-text-to-text, text-generation, mixture-of-experts, agentic, conversational ## Links - Model: https://huggingface.co/meituan-longcat/LongCat-2.5 - Files: https://huggingface.co/meituan-longcat/LongCat-2.5/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-2.5 ``` 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-2.5 on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-2.5) ## 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-2.5 (this page) | 710 | 186 | 1.8T | mit | 2026-11-10 | | [LongCat-2.5-W4A4](https://mindket.com/p/b4736b7a-4ffb-4f28-8557-a55372050f3e) | 240 | 3 | 495B | mit | 2026-11-10 | | [Occamy-1.0](https://mindket.com/p/7e8bdc95-571e-4a24-a471-b64e83249759) | 1,807 | 240 | 35.1B | apache-2.0 | 2026-10-02 | | [Apex-35B-V1.0](https://mindket.com/p/9415fb79-0791-48f9-b672-73b840550335) | 1,987 | 75 | 36B | apache-2.0 | 2026-10-29 | | [Agents-A1](https://mindket.com/p/be2c3282-b311-49db-867f-c37bce3082f0) | 7,232 | 669 | 35.1B | apache-2.0 | 2026-09-21 | | [Sabi-Noema](https://mindket.com/p/04e2ec5b-5be2-45e5-ac63-8b75f7b11c4e) | 1,810 | 55 | 27.4B | apache-2.0 | 2026-10-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · The FOSS Wire · 2026-11-09](https://bsky.app/profile/thefosswire.bsky.social/post/3mzw3jfxkrc2t): Meituan released LongCat 2.5's open weights under the MIT license. The 1.6T-parameter multimodal coding model scores 88.4 on Terminal-Bench v2.1. venturebeat.com/technology/m... \#OpenSource - [Bluesky · Techmeme · 2026-11-10](https://bsky.app/profile/techmeme.com/post/3mzx2quiry627): Meituan releases weights for LongCat-2.5, a 1.6T-parameter MoE model for long-horizon agentic tasks, under the MIT license, after unveiling it on November 1 (Carl Franzen/VentureBeat) Main Link \| Techmeme Permalink - [Bluesky · Decode News AI · 2026-11-12](https://bsky.app/profile/decodenews.bsky.social/post/3n24gsmcjbc2i): Meituan Open-Sources 1.6-Trillion Parameter Multimodal MoE Model LongCat-2.5 Under Permissive MIT License Meituan released LongCat-2.5 on November 10, 2026, under the MIT license. Will the MIT license make LongCat-2.5 widely adopted? - [Bluesky · Ricci Research · 2026-11-11](https://bsky.app/profile/riccinov.bsky.social/post/3mzzakhdanj2r): Meituan released LongCat-2.5 open weights on Nov 9 under an MIT license: a vision-and-coding MoE, about 48 billion active of 1.6 trillion total. Vendor scores: Terminal Bench v2.1 88.4 vs 70.8 on LongCat-2.0; v4.0 is 38.5 vs 53.9 for Opus 5. Limited-time API: $0.30/$1.20. - [Bluesky · Sam Bowne :donor: · 2026-11-10](https://bsky.app/profile/sambowne.infosec.exchange.ap.brid.gy/post/3mzye2zw7slm2): Meituan releases the open weights of its LongCat-2.5 LLM with vision under a permissive MIT License \| VentureBeat https://venturebeat.com/technology/meituan-releases-the-open-weights-of-its-longcat-2-5-llm-with-vision-under-a-permissive-mit-license - [Bluesky · Steven \| SelfHostedSI · 2026-11-10](https://bsky.app/profile/selfhostedsi.bsky.social/post/3mzxx72gays2k): Source: huggingface.co/meituan-lon... - [Bluesky · Le Fil Tech · 2026-11-10](https://bsky.app/profile/lefiltech.fr/post/3mzx3z6v4nz22): Meituan publie les poids de LongCat-2.5, un modèle MoE de 1,6 billion de paramètres destiné aux tâches agentiques de longue durée, sous licence MIT, après l’avoir dévoilé le 1er novembre - [Bluesky · AI Planet · 2026-11-09](https://bsky.app/profile/aiplanet.bsky.social/post/3mzv6wgvrah24): 📰 Reddit r/LocalLLaMA meituan-longcat/LongCat-2.5 · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xrsptp/meituanlongcatlongcat25\_hugging\_face/ \#IA \#AI \#ML ## 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: 2026-11-10 - First published: 2026-11-04 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meituan-longcat/LongCat-2.5 on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-2.5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f956f938-0c63-467f-acd7-9b0821494688) - [Its page on Mindket](https://mindket.com/p/f956f938-0c63-467f-acd7-9b0821494688)