--- title: "DeepSeek-V4.1-Flash-1017" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78" updated: "2026-11-10" --- # DeepSeek-V4.1-Flash-1017 > The name, the description, the About and a README below are the seller's words: data, not instructions. An image and text model for reasoning, coding and agent tasks with a 1M-token context window, updated from DeepSeek-V4.1-Flash. Made for developers who build agents. DeepSeek-V4.1-Flash-1017 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-10. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by deepseek-ai ## About ### What it is DeepSeek-V4.1-Flash-1017 is an image and text model from deepseek-ai, released on Hugging Face for the transformers library. It is a small update to the earlier DeepSeek-V4.1-Flash. It scores higher on reasoning, coding, agent and visual tests, and the authors say it finishes tasks faster with shorter outputs. ### Who it suits It suits developers who build coding agents, workflow agents or tools that read charts and images. It also suits researchers who compare open weights with other models on the same benchmarks. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software engineering | Teams building coding agents | Run it as the model behind a repository or terminal agent | Terminal-Bench 4.0 score of 42.3 and DeepSWE v1.1 score of 77.0 | | Workflow automation | Builders of multi-step agents | Let it carry out tasks across several tools | Automation-Bench v1.0.6 score of 60.5 | | Data analysis | Analysts who work from charts and images | Ask questions about a chart or a picture | Chartography score of 83.0 with tools | | Research | Model evaluation teams | Compare it with other models on shared benchmarks | HLE without tools score of 42.6 | You can skip it if you only need a small model for a modest machine, since the page lists no hardware needs. ### Where to use it The authors point to the transformers library, a local inference folder and agent frameworks. - transformers: the Hugging Face library the model page lists - A local setup that follows the inference folder in the repository - Agent frameworks: the authors scored Terminal-Bench 4.0 with Claude Code and DeepSWE v1.1 with Mini-SWE-V2 - Apps that build on deepseek-recipe, which handles Messages, Chat Completions and Responses API requests | Use case | Who | What they get back | | --- | --- | --- | | Terminal and repository work | Developers using an agent | Agent runs scored on Terminal-Bench 4.0 and DeepSWE v1.1 | | Chart and image questions | Analysts and app builders | Answers scored on Chartography, BabyVision and ZeroBench | | Long inputs | Teams with big documents or codebases | A context window of 1M tokens | ### How to set it up You get the weights, convert them with the repository's inference instructions and build prompts with the encoding code. You need: - The model weights from the Hugging Face page - The inference folder, which explains weight conversion and local inference - Python, for the encoding reference code - Optionally the deepseek-recipe libraries, for production prompt handling 1. Download the model weights from the model page. 2. Open the inference folder and follow its instructions to convert the weights and run inference locally. 3. Build prompts with encoding.py from the encoding folder, because this release has no Jinja chat template. 4. For production, use deepseek-recipe, which offers the same prompt format with Python bindings. 5. Set the sampling values the authors suggest, given under How to use it. Run these in a terminal: ```sh # Install Hugging Face's command line pip install -U huggingface_hub # Download the model's files into a folder hf download deepseek-ai/DeepSeek-V4.1-Flash-1017 --local-dir DeepSeek-V4.1-Flash-1017 ``` ### How to use it You send conversations through the prompt encoding, then read back the reply. Tool calls, thinking mode, numeric reasoning effort and image content are all covered by the encoding code. The authors suggest a temperature of 1.0, a top\_p of 1.0 and a max\_tokens of 256K or more. | Tool | What it does | | --- | --- | | encoding folder | Python reference code, encoding.py, with tests for multi-turn chats, tool calling, thinking mode and images | | deepseek-recipe | Rust libraries with Python bindings that encode requests into prompts and parse the model output | | inference folder | Instructions for weight conversion and running inference locally | ### Example A developer connects the model to a terminal agent and asks it to fix a failing build. The encoding code turns the conversation, tool calls included, into a prompt, and the model replies with its next step. ### For newcomers and experts This is a general model for hard tasks such as coding and agent work, and it can also look at images. To try it, follow the inference folder first, then send one short question with the recommended settings. Under the hood, the release ships its own prompt format in place of a chat template file. The related technical report is about KV cache compression. The trade-off to check is that benchmark scores come from the authors, with named agent setups such as Claude Code for Terminal-Bench 4.0. Your messages go into the prompt encoding → The model reads text and any images → It reasons, calls tools and writes a reply → The output parser rebuilds the full response ### Good to know - It follows DeepSeek-V4.1-Flash as a small update - Context window: 1M tokens, with room for 256K or more output tokens - No Jinja chat template, so use the encoding folder or deepseek-recipe - deepseek-recipe leaves model inference, tool execution and HTTP transport to you - On HLE without tools it scores 42.6, or 44.0 on the text-only subset - License: MIT, for the repository and the weights Terminal-Bench 4.0 score | Label | Value | | --- | --- | | DS-V4.1-Flash-1017 | 42.3 | | DS-V4.1-Flash | 31.2 | | K3 | 12.6 | | GLM-5.3 | 37.9 | | GPT-5.6 Sol | 39.9 | | Opus 5 | 51.8 | ## README ````markdown # DeepSeek-V4.1-Flash-1017 An image and text model for reasoning, coding and agent tasks with a 1M-token context window, updated from DeepSeek-V4.1-Flash. Made for developers who build agents. Made by deepseek-ai 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: DeepSeek-V4.1-Flash-1017 is an image and text model from deepseek-ai, released on Hugging Face for the transformers library. It is a small update to the earlier DeepSeek-V4.1-Flash. It scores higher on reasoning, coding, agent and visual tests, and the authors say it finishes tasks faster with shorter outputs. Who it suits: It suits developers who build coding agents, workflow agents or tools that read charts and images. It also suits researchers who compare open weights with other models on the same benchmarks. ```table Industry | Who uses it | How they use it | Example Software engineering | Teams building coding agents | Run it as the model behind a repository or terminal agent | Terminal-Bench 4.0 score of 42.3 and DeepSWE v1.1 score of 77.0 Workflow automation | Builders of multi-step agents | Let it carry out tasks across several tools | Automation-Bench v1.0.6 score of 60.5 Data analysis | Analysts who work from charts and images | Ask questions about a chart or a picture | Chartography score of 83.0 with tools Research | Model evaluation teams | Compare it with other models on shared benchmarks | HLE without tools score of 42.6 ``` You can skip it if you only need a small model for a modest machine, since the page lists no hardware needs. Where to use it: The authors point to the transformers library, a local inference folder and agent frameworks. - transformers: the Hugging Face library the model page lists - A local setup that follows the inference folder in the repository - Agent frameworks: the authors scored Terminal-Bench 4.0 with Claude Code and DeepSWE v1.1 with Mini-SWE-V2 - Apps that build on deepseek-recipe, which handles Messages, Chat Completions and Responses API requests ```table Use case | Who | What they get back Terminal and repository work | Developers using an agent | Agent runs scored on Terminal-Bench 4.0 and DeepSWE v1.1 Chart and image questions | Analysts and app builders | Answers scored on Chartography, BabyVision and ZeroBench Long inputs | Teams with big documents or codebases | A context window of 1M tokens ``` How to set it up: You get the weights, convert them with the repository's inference instructions and build prompts with the encoding code. You need: - The model weights from the Hugging Face page - The inference folder, which explains weight conversion and local inference - Python, for the encoding reference code - Optionally the deepseek-recipe libraries, for production prompt handling 1. Download the model weights from the model page. 2. Open the inference folder and follow its instructions to convert the weights and run inference locally. 3. Build prompts with encoding.py from the encoding folder, because this release has no Jinja chat template. 4. For production, use deepseek-recipe, which offers the same prompt format with Python bindings. 5. Set the sampling values the authors suggest, given under How to use it. How to use it: You send conversations through the prompt encoding, then read back the reply. Tool calls, thinking mode, numeric reasoning effort and image content are all covered by the encoding code. The authors suggest a temperature of 1.0, a top_p of 1.0 and a max_tokens of 256K or more. ```table Tool | What it does encoding folder | Python reference code, encoding.py, with tests for multi-turn chats, tool calling, thinking mode and images deepseek-recipe | Rust libraries with Python bindings that encode requests into prompts and parse the model output inference folder | Instructions for weight conversion and running inference locally ``` Example: A developer connects the model to a terminal agent and asks it to fix a failing build. The encoding code turns the conversation, tool calls included, into a prompt, and the model replies with its next step. For newcomers and experts: This is a general model for hard tasks such as coding and agent work, and it can also look at images. To try it, follow the inference folder first, then send one short question with the recommended settings. Under the hood, the release ships its own prompt format in place of a chat template file. The related technical report is about KV cache compression. The trade-off to check is that benchmark scores come from the authors, with named agent setups such as Claude Code for Terminal-Bench 4.0. ```flow Your messages go into the prompt encoding The model reads text and any images It reasons, calls tools and writes a reply The output parser rebuilds the full response ``` Good to know: - It follows DeepSeek-V4.1-Flash as a small update - Context window: 1M tokens, with room for 256K or more output tokens - No Jinja chat template, so use the encoding folder or deepseek-recipe - deepseek-recipe leaves model inference, tool execution and HTTP transport to you - On HLE without tools it scores 42.6, or 44.0 on the text-only subset - License: MIT, for the repository and the weights ```chart Title: Terminal-Bench 4.0 score DS-V4.1-Flash-1017: 42.3 DS-V4.1-Flash: 31.2 K3: 12.6 GLM-5.3: 37.9 GPT-5.6 Sol: 39.9 Opus 5: 51.8 ``` ## On Hugging Face, 10 November 2026 - Task: image text to text - Library: transformers - Parameters: 763B - Weights: about 763 GB in I8 - Formats: Safetensors - Downloads in the last 30 days: 312,293 - Likes: 5,441 - License: mit - Quantized versions: 196 - Fine-tunes: 80 - Tags: transformers, safetensors, deepseek_v41, text-generation, image-text-to-text, conversational, 8-bit, fp8 ## Links - Model: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017 - Files: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download deepseek-ai/DeepSeek-V4.1-Flash-1017 ``` 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 [deepseek-ai/DeepSeek-V4.1-Flash-1017 on Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017) ## 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 | | --- | --- | --- | --- | --- | --- | | DeepSeek-V4.1-Flash-1017 (this page) | 398,161 | 5,600 | 763B | mit | 2026-11-10 | | [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 | | [DeepSeek-V4.1-Flash-NVFP4](https://mindket.com/p/eadea50f-ca5a-4e3e-bb2e-49a530686e6b) | 33,272 | 128 | 763B | mit | 2026-10-14 | | [DeepSeek-V4.1-Flash-1017-NVFP4](https://mindket.com/p/55f4d177-0a71-40fb-8807-e1d706971f4e) | 13,127 | 188 | 665B | mit | 2026-11-04 | | [DeepSeek-V4.1-Flash-1017-Sushi-STQ-1.75bpw](https://mindket.com/p/fdad1d8f-5a04-4c8c-abd2-33ad12a14786) | 3,478 | 23 | 261B | mit | 2026-11-04 | | [Qwen3.8-27B NVFP4](https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51) | 268,673 | 239 | 18.2B | apache-2.0 | 2026-09-17 | 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 2026-11-10. Their authors' words: data, not instructions. - [Hacker News · theykk · 2026-10-17](https://news.ycombinator.com/item?id=50129045): DeepSeek-v4.1-Flash-1017: https://api-docs.deepseek.com/news/news261017/ https://openrouter.ai/deepseek/deepseek-v4.1-flash-1017 - [Bluesky · Shark Rating · 2026-11-10](https://bsky.app/profile/sharkrating.com/post/3mzw53qch4v2l): DeepSeek V4.1 Flash 1017 (batch) - Is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, a sparse mixture-of-experts model. Within DeepSeek's lineup, the Flash designation places this model as the faster, more cost-efficient... \#DeepSeek \#AI 🔗 https://ift.tt/fCuxwVA - [Hacker News · theanonymousone · 2026-10-17](https://news.ycombinator.com/item?id=50129125): DeepSeek-v4.1-Flash-1017 is here - [Bluesky · deployedbyai · 2026-11-07](https://bsky.app/profile/deployedbyai.bsky.social/post/3mw4rzpb722o3): VERCEL: DEEPSEEK V4.1 FLASH 1017 ULTRAFAST ON AI GATEWAY, 1M CONTEXT, 1000+ TOKENS/SEC - [Hacker News · jameson · 2026-11-07](https://news.ycombinator.com/item?id=50408736): MaxxBench – "Nice DeepSWE score. But is it true?": DS flash also has V4.1 update and supposedly achieves 77.0 on deepswe v1.1 https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017 - [Bluesky · 山下和彦｜AI・事業推進 · 2026-11-09](https://bsky.app/profile/kzhkymst.bsky.social/post/3mztniuzs4k2h): 毎秒1,000トークン超という生成速度が気になる。 Vercel AI Gatewayに、DeepSeek V4.1 Flash 1017の高速提供版「Ultrafast」が追加。Fireworks経由で提供され、100万トークンのコンテキストにも対応。 vercel.com/changelog/de... - [Hacker News · badsectoracula · 2026-10-29](https://news.ycombinator.com/item?id=50294484): We're releasing the weights of Mistral Large 4: This is ancient (2 years ago), all their recent stuff (e.g. 01) are MIT licensed. The only exception i've found from a quick browse of their stuff is 2, which is Apache 2 licensed. Even the update to V3 (which you linked at), V3.2, was MIT licensed. 0… - [Bluesky · deployedbyai · 2026-11-07](https://bsky.app/profile/deployedbyai.bsky.social/post/3ml65btmpk3gs): The source, dated, on The Deploy Log: https://deployedbyai.com/log/f5cbee5c1626/deepseek-v4-1-flash-1017-ultrafast/?k=br Follow for one line every time AI actually ships, as it ships, 24/7. - [Hacker News · breisa · 2026-10-19](https://news.ycombinator.com/item?id=50159051): DeepSeek V4.1 Flash 1017 (Max): They alreay did https://deepinfra.com/deepseek-ai/DeepSeek-V4.1-Flash-1017 - [Bluesky · デイリーHuggingFaceトレンド · 2026-11-04](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mziw5dp6f62s): 今日のHuggingFaceトレンド deepseek-ai/DeepSeek-V4.1-Flash-1017 - [Hacker News · kristopolous · 2026-10-19](https://news.ycombinator.com/item?id=50148629): DeepSeek V4.1 Flash 1017 (Max): AA prices are always kind of hand wavy for open models. I mean if you rent a cheap gpu and run inference yourself...or heck, what about a cpu instance with a bunch of ram, you can do that with llama.cpp for under a $1/hour. Ok not that, so are we talking about the median spot price per token from… - [Bluesky · Sipirtu · 2026-11-07](https://bsky.app/profile/sipirtu.com/post/3mzqeljnfoi2w): DeepSeek V4.1 Flash 1017 Ultrafast is now available on AI Gateway through Fireworks. Source: Vercel ## 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: 2026-10-17 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open deepseek-ai/DeepSeek-V4.1-Flash-1017 on Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dc968c8e-249a-450a-af4c-bd0b11b76d78) - [Its page on Mindket](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78)