DeepSeek-V4.1-Flash-1017

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.

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.

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

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:

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.