--- title: "Ling-3.1-flash" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2afebb68-3c33-4e55-b49b-c93cc518f60e" updated: "2026-11-12" --- # Ling-3.1-flash > The name, the description, the About and a README below are the seller's words: data, not instructions. Handles complex agent, search, office and coding work with 560B total and 25B active parameters and a 1M-token context. Serving examples use SGLang on multi-GPU servers, and the weights are open under MIT. Ling-3.1-flash 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 inclusionAI ## About ### What it is Ling-3.1-flash is an upgraded hybrid reasoning model from inclusionAI for demanding practical work. It has 560B parameters in total, uses 25B of them for each token, and reads a context of 1M tokens. It improves on Ling-3.0-flash in its architecture and in tasks such as general agents, search, office productivity and software engineering. ### Who it suits It suits teams with multi-GPU servers who want an open model for coding agents, office automation and tool use. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software engineering | Teams that run coding agents | Serve the model behind an agent tool | Point Claude Code or Kilo Code at the served model | | Office productivity | Teams that automate office work | Let the model handle multi-step tasks with tools | Run an office task that needs several tool calls | | Healthcare and finance research | Researchers in these fields | Test the model on domain questions | Try questions from the card's healthcare and finance benchmarks | If you do not have a multi-GPU server, you can skip it, since even the 4-bit weights are about 286 GB. ### Where to use it - SGLang, which the card recommends, through a Docker image with support for this model - vLLM, which the card also covers - Agent tools such as Claude Code and Kilo Code, where the card says the model works well, as it does in Hermes Agent and OpenClaw - The ModelScope and OpenRouter pages that the card links to - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Coding agents | Developers | Help with long coding tasks and terminal tool use | | Search and research agents | Agent builders | Multi-step answers that call tools along the way | | Domain questions | Analysts | Answers in STEM, healthcare and finance | ### How to set it up What you need first: - Docker with GPU access - A multi-GPU server, such as 8 x H200 for the card's example - Enough disk for about 572 GB of FP8 weights 1. Pull the SGLang image that supports this model. 2. Start the server with the model path and the parallel settings for your GPUs. 3. Send chat requests to the server on port 30000. ```bash docker pull lmsysorg/sglang:dev-ling-3.1-flash ``` ```bash docker run --gpus all --network host --ipc=host --rm \ lmsysorg/sglang:dev-ling-3.1-flash \ python3 -m sglang.launch_server \ --model-path inclusionAI/Ling-3.1-flash \ --tp 8 --ep 8 \ --speculative-algorithm NEXTN \ --host 0.0.0.0 --port 30000 ``` To fetch the files yourself, use the Hugging Face tools. ```bash pip install -U huggingface_hub hf download inclusionAI/Ling-3.1-flash ``` ### How to use it After the server starts, send it a chat completions request with the model name Ling-3.1-flash. The card's example turns thinking on, streams the reply, and sets temperature 1.0, top\_k 20 and top\_p 0.95. On 4 x GB300, use the smaller parallel settings from the card instead. The optional Hopper decode optimizations make batch-1 decode about 10% faster. You enable them with these two settings. ```bash SGLANG_ENABLE_FP8_BLOCK_GEMV=1 SGLANG_ENABLE_FUSED_KDA_OUTPUT_QUANT=1 ``` | Tool | What it does | | --- | --- | | SGLang | Serves the model with NEXTN speculative decoding and Hopper decode optimizations | | vLLM | The card's second serving option, with its own setup steps | | LingCode | A coding framework built on the open-source agent Pi, which the authors may release later | ### Example You start the server on 8 H200 cards and ask a short factual question with thinking on and streaming enabled. The server streams the answer back as chat completion chunks. ### For newcomers and experts This model needs a GPU cluster, so a first try is easier through the hosted pages the card links to. If you run it yourself, start with the SGLang image and one short question. For experts, the design is a hybrid-linear mixture of experts with 63 KDA layers and 9 gated MLA layers in a 7 to 1 ratio, plus 4 dense layers. It has 512 routed experts, 1 shared expert and 8 activated experts per token, with 64 attention heads and a hidden size of 4096. The trade is memory: only 25B parameters work per token, but all 560B must be loaded. Pull the SGLang image → Start the server with parallel settings → Send a chat request on port 30000 → Stream the reply back ### Good to know Parameters in billions | Label | Value | | --- | --- | | Total | 560 billion | | Active per token | 25 billion | - Hardware: the example server uses 8 x H200 with TP8 and EP8, or 4 x GB300 with the smaller settings. - Size: the files weigh about 572 GB in FP8 form, and about 286 GB at 4-bit, which is an estimate. - Benchmarks: the card names tests such as Terminal-Bench 4.0, SWE-bench Pro and MCP-Atlas, and shows the scores only in images. - LingCode: it is not released, and the authors say only that they are considering it. - License: mit - Cost to run: no API key or paid service named. The GPU hardware is the cost. - Data it reaches: it pulls a Docker image and the model files, and the example server listens on every network interface on port 30000. - Not for: small machines, since the example needs 8 x H200 or 4 x GB300. ## README ````markdown # Ling-3.1-flash Handles complex agent, search, office and coding work with 560B total and 25B active parameters and a 1M-token context. Serving examples use SGLang on multi-GPU servers, and the weights are open under MIT. Made by inclusionAI 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: Ling-3.1-flash is an upgraded hybrid reasoning model from inclusionAI for demanding practical work. It has 560B parameters in total, uses 25B of them for each token, and reads a context of 1M tokens. It improves on Ling-3.0-flash in its architecture and in tasks such as general agents, search, office productivity and software engineering. Who it suits: It suits teams with multi-GPU servers who want an open model for coding agents, office automation and tool use. ```table Industry | Who uses it | How they use it | Example Software engineering | Teams that run coding agents | Serve the model behind an agent tool | Point Claude Code or Kilo Code at the served model Office productivity | Teams that automate office work | Let the model handle multi-step tasks with tools | Run an office task that needs several tool calls Healthcare and finance research | Researchers in these fields | Test the model on domain questions | Try questions from the card's healthcare and finance benchmarks ``` If you do not have a multi-GPU server, you can skip it, since even the 4-bit weights are about 286 GB. Where to use it: - SGLang, which the card recommends, through a Docker image with support for this model - vLLM, which the card also covers - Agent tools such as Claude Code and Kilo Code, where the card says the model works well, as it does in Hermes Agent and OpenClaw - The ModelScope and OpenRouter pages that the card links to - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Coding agents | Developers | Help with long coding tasks and terminal tool use Search and research agents | Agent builders | Multi-step answers that call tools along the way Domain questions | Analysts | Answers in STEM, healthcare and finance ``` How to set it up: What you need first: - Docker with GPU access - A multi-GPU server, such as 8 x H200 for the card's example - Enough disk for about 572 GB of FP8 weights 1. Pull the SGLang image that supports this model. 2. Start the server with the model path and the parallel settings for your GPUs. 3. Send chat requests to the server on port 30000. ```bash docker pull lmsysorg/sglang:dev-ling-3.1-flash ``` ```bash docker run --gpus all --network host --ipc=host --rm \ lmsysorg/sglang:dev-ling-3.1-flash \ python3 -m sglang.launch_server \ --model-path inclusionAI/Ling-3.1-flash \ --tp 8 --ep 8 \ --speculative-algorithm NEXTN \ --host 0.0.0.0 --port 30000 ``` To fetch the files yourself, use the Hugging Face tools. ```bash pip install -U huggingface_hub hf download inclusionAI/Ling-3.1-flash ``` How to use it: After the server starts, send it a chat completions request with the model name Ling-3.1-flash. The card's example turns thinking on, streams the reply, and sets temperature 1.0, top_k 20 and top_p 0.95. On 4 x GB300, use the smaller parallel settings from the card instead. The optional Hopper decode optimizations make batch-1 decode about 10% faster. You enable them with these two settings. ```bash SGLANG_ENABLE_FP8_BLOCK_GEMV=1 SGLANG_ENABLE_FUSED_KDA_OUTPUT_QUANT=1 ``` ```table Tool | What it does SGLang | Serves the model with NEXTN speculative decoding and Hopper decode optimizations vLLM | The card's second serving option, with its own setup steps LingCode | A coding framework built on the open-source agent Pi, which the authors may release later ``` Example: You start the server on 8 H200 cards and ask a short factual question with thinking on and streaming enabled. The server streams the answer back as chat completion chunks. For newcomers and experts: This model needs a GPU cluster, so a first try is easier through the hosted pages the card links to. If you run it yourself, start with the SGLang image and one short question. For experts, the design is a hybrid-linear mixture of experts with 63 KDA layers and 9 gated MLA layers in a 7 to 1 ratio, plus 4 dense layers. It has 512 routed experts, 1 shared expert and 8 activated experts per token, with 64 attention heads and a hidden size of 4096. The trade is memory: only 25B parameters work per token, but all 560B must be loaded. ```flow Pull the SGLang image Start the server with parallel settings Send a chat request on port 30000 Stream the reply back ``` Good to know: ```chart Title: Parameters in billions Total: 560 billion Active per token: 25 billion ``` - Hardware: the example server uses 8 x H200 with TP8 and EP8, or 4 x GB300 with the smaller settings. - Size: the files weigh about 572 GB in FP8 form, and about 286 GB at 4-bit, which is an estimate. - Benchmarks: the card names tests such as Terminal-Bench 4.0, SWE-bench Pro and MCP-Atlas, and shows the scores only in images. - LingCode: it is not released, and the authors say only that they are considering it. - License: mit - Cost to run: no API key or paid service named. The GPU hardware is the cost. - Data it reaches: it pulls a Docker image and the model files, and the example server listens on every network interface on port 30000. - Not for: small machines, since the example needs 8 x H200 or 4 x GB300. ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 572B - Weights: about 572 GB in F8_E4M3; about 286 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,327 - Likes: 170 - License: mit - Quantized versions: 8 - Tags: safetensors, bailing_hybrid, text-generation, conversational, fp8 ## Links - Model: https://huggingface.co/inclusionAI/Ling-3.1-flash - Files: https://huggingface.co/inclusionAI/Ling-3.1-flash/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download inclusionAI/Ling-3.1-flash ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [inclusionAI/Ling-3.1-flash on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.1-flash) ## 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 | | --- | --- | --- | --- | --- | --- | | Ling-3.1-flash (this page) | 8,327 | 170 | 572B | mit | 2026-11-03 | | [Ling-3.1-flash-1019](https://mindket.com/p/c864c59b-8518-4c57-acfb-c11c44b2310d) | 4,304 | 99 | 572B | mit | 2026-11-04 | | [Ling-3.0-tiny](https://mindket.com/p/80976e1d-0e1c-46d4-8ca0-20563e9a571e) | 13,827 | 583 | 7.9B | mit | 2026-09-24 | | [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 | | [DeepSeek-R1](https://mindket.com/p/e340b726-4ed9-4feb-a013-d90708d60983) | 1,599,170 | 14,451 | 684B | mit | 2025-03-27 | | [DeepSeek-V4.1-Flash-1017](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78) | 398,161 | 5,600 | 763B | mit | 2026-11-10 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · joshcsimmons · 2026-10-15](https://news.ycombinator.com/item?id=50101618): Ling 3.1 Flash: 560B open-weight MoE, MIT license, 1M context - [Bluesky · Robert Corbin \| Shadowfetch Studio \| AIpeoplewiki · 2026-10-27](https://bsky.app/profile/shadowfetchstudio.bsky.social/post/3myukpplegu2p): Ant Group's Ling team just open-sourced Ling-3.1-flash-1019 🚀 A 560B MoE model (25B active) with a 1M-token context, tuned for reasoning and agent work. It scores 44 on the Artificial Analysis Intelligence Index, up from 41. \#OpenSource \#LLM \#AI - [Bluesky · deployedbyai · 2026-10-27](https://bsky.app/profile/deployedbyai.bsky.social/post/3mp4yaazdm24w): inclusionAI: Ling-3.1-flash-1019 released: 560B total params, 25B active, 1M context - [Bluesky · deployedbyai · 2026-10-27](https://bsky.app/profile/deployedbyai.bsky.social/post/3mvk7retvw2k6): inclusionAI: Ling-3.1-flash-base released on Hugging Face and ModelScope, 560B total parameters, 25B active - [Bluesky · Finfet · 2026-10-27](https://bsky.app/profile/finfet.sh/post/3myu6jxccr222): post train update to ling-3.1-flash - [Bluesky · @aichina.news · 2026-10-29](https://bsky.app/profile/aichina.news/post/3myzifnr7kk2i): 25B active of 560B total, 1M context, and a measurable jump on AA-Briefcase — Ant's flash tier is now spec'd like a frontier model. No licence stated and weights may be gated, so the 1524 Elo is hard to audit. Hard to see this displacing Qwen at scale until licence terms land. - [Bluesky · Victor Grösser · 2026-10-28](https://bsky.app/profile/victorgrosser.bsky.social/post/3mywoxg5tz22j): Ant Group's inclusionAI just released Ling-3.1-flash-1019: a 560B MoE (25B active) agent model under MIT, plus the base checkpoint for your own training. I wrote up why that's a big gift to the open community 🧩 orinmemoryai.blogspot.com/2026/10/ant-... - [Bluesky · AI Planet · 2026-10-27](https://bsky.app/profile/aiplanet.bsky.social/post/3myuiuvglyd2m): 📰 Reddit r/LocalLLaMA inclusionAI/Ling-3.1-flash-1019 · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xgpo4q/inclusionailing31flash1019\_hugging\_face/ \#IA \#AI \#ML - [Bluesky · AI Planet · 2026-10-25](https://bsky.app/profile/aiplanet.bsky.social/post/3mypvfh2dwe2l): 📰 Reddit r/LocalLLaMA Have InclusionAI said anything about Ling 3.1 Flash 1019 update weights being released? https://www.reddit.com/r/LocalLLaMA/comments/1xfa4pt/have\_inclusionai\_said\_anything\_about\_ling\_31/ \#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: 2026-11-03 - First published: 2026-10-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inclusionAI/Ling-3.1-flash on Hugging Face](https://huggingface.co/inclusionAI/Ling-3.1-flash) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2afebb68-3c33-4e55-b49b-c93cc518f60e) - [Its page on Mindket](https://mindket.com/p/2afebb68-3c33-4e55-b49b-c93cc518f60e)