DeepSeek-V4.1-Flash-1017: Update boosts coding and agent performance
DeepSeek has released a minor update to its V4.1-Flash model, available for free download under the MIT license. According to the maker, it improves reasoning, coding, agent tasks and image understanding while using less time and shorter outputs.
What it's about
DeepSeek has released the model DeepSeek-V4.1-Flash-1017 on Hugging Face. It is a minor update to the already released DeepSeek-V4.1-Flash. According to the Model Card, it brings clear improvements in reasoning, coding, agent tasks (the model works on its own with tools and multiple steps) and image benchmarks. It is also said to finish tasks faster and produce shorter outputs, so they cost less. The weights are available for download under the MIT license. At the time of capture, the repo had about 52,000 downloads and over 2,300 likes.
Key technical details
Hugging Face lists the model at about 763.2B parameters (parameters are, roughly speaking, the adjustable settings the model learned during training). The weights come as safetensors in 8-bit format (FP8). The pipeline is "image-text-to-text", so the model takes in text and images and outputs text. The library is transformers, and the model type is deepseek_v41. According to the page, there is no GGUF version (a format for local tools like llama.cpp). The title of the accompanying technical report is "Pushing the Limits of KV Cache Compression". The KV cache is the buffer where the model stores what it has already processed in long texts. Compressing it is a focus of the model family, but the Model Card gives no details here.
Benchmarks according to the maker
DeepSeek compares the update with its predecessor and with K3, GLM-5.3, GPT-5.6 Sol and Opus 5. These figures come from the maker and were not independently verified here.
| Benchmark | V4.1-Flash-1017 | V4.1-Flash | Opus 5 | GPT-5.6 Sol |
|---|---|---|---|---|
| HLE without tools | 42.6 | 36.8 | 56.3 | 44.5 |
| Terminal-Bench 4.0 | 42.3 | 31.2 | 51.8 | 39.9 |
| DeepSWE v1.1 | 77.0 | 74.2 | 74.0 | 73.0 |
| ProgramBench | 29.1 | 20.3 | 37.0 | 23.0 |
| NL2Repo-Bench | 68.2 | 65.4 | 75.3 | 56.8 |
| Automation-Bench v1.0.6 | 60.5 | 54.8 | 50.3 | 45.8 |
| Agents' Last Exam | 35.4 | 31.8 | 28.6 | 26.7 |
| Apex Bench v1.1 | 72.1 | 64.6 | 65.8 | 54.3 |
On the image benchmarks (with tools), the model scores 83.0 on Chartography, 92.5 on BabyVision and 51.0 on ZeroBench-main. For HLE, there is also a score for the text-only portion (44.0). Terminal-Bench 4.0 used the Claude Code harness, and DeepSWE used the Mini-SWE-V2 harness (a harness is the environment in which the model calls tools). In several agent and coding tests, the table shows the model ahead of Opus 5 and GPT-5.6 Sol. On HLE, Terminal-Bench, ProgramBench and NL2Repo, though, it trails Opus 5.
Prompt format and deployment
This time there is no Jinja chat template. Instead, the encoding folder contains a Python reference implementation with test cases for multi-turn conversations, tool calls, thinking mode, numeric reasoning effort, system messages mid-conversation and embedded images. For production use, DeepSeek also offers deepseek-recipe, Rust libraries with Python bindings. They convert requests in the Messages, Chat Completions and Responses API formats into the DeepSeek format, encode them into prompts or token IDs and parse the model output back, including streamed output. Inference, tool execution and HTTP transport remain the caller's job. Notes on weight conversion and local inference are in the inference folder.
Recommended sampling settings: temperature 1.0, top_p 1.0, a 1M-token context window, max_tokens of at least 256K.
What you need to try it
Despite "Flash" in the name, the model is huge. At 763B parameters and FP8, the weight files come to about 510 GB according to Hugging Face, and that is memory needed for the weights alone. That is only realistic with a multi-GPU server (several data-center GPUs). It isn't suitable for home PCs, Macs or Google Colab, and there is no ready-made quantized GGUF version yet. If you want to test it, your best bet is DeepSeek's chat site or a cloud provider. Also plan for about 520 GB of free disk space if you download the weights.
Our take
The update is an intermediate step, but according to the maker, the jumps in agent and coding tests are big, such as over 11 points on Terminal-Bench. Whether independent tests confirm this remains to be seen, since all the numbers come from DeepSeek itself and the comparison models were measured with partly different harnesses. The Model Card gives no figures to back up the claim of shorter output and lower costs. The open MIT license at this size is interesting: it lets companies and researchers run a near-frontier model themselves. The heavy hardware demands limit that to organizations with the right infrastructure, though.
Sources
- Hugging Face Trending huggingface.co
- Interconnects (offene Modelle) interconnects.ai
- Google News (JP) news.google.com
- r/LocalLLaMA huggingface.co
Try it yourself: DeepSeek-V4.1-Flash-1017
Explained step by step, no prior knowledge needed – on your own computer or free in your browser.
What can I do with it?
The model understands text and images and, according to the maker, is especially suited to coding, multi-step agent tasks, reasoning and analyzing charts. It supports thinking mode, tool calls and a 1M-token context window.
Who is it worth it for?
For teams with multi-GPU servers or cloud access who want to run an open frontier model themselves. It isn't suitable for normal home PCs.
What your computer needs
There is only the FP8 variant with about 763B parameters. According to the source, there is no smaller version or GGUF. Individual users are better off with the DeepSeek chat site or an API.
- Weights in FP8: roughly 510 GB, plus memory for the KV cache.
- Realistic only with several data-center GPUs.
- Long context (up to 1M tokens) costs extra memory.
- It won't run on a standard laptop, Mac or Colab.
Huge (763,2 B parameters): locally, practically only on servers with multiple GPUs. Colab Free isn't enough.
How do I find out what my computer has?
- Windows: Press Ctrl + Shift + Esc → Task Manager → Performance tab → click GPU on the left. At the bottom you'll see Dedicated GPU memory (that's your VRAM). You'll find system memory under Memory.
- Mac: Apple menu at the top left → About This Mac. You'll see your chip (e.g. M2) and memory (e.g. 16 GB). On Apple chips, graphics and processor share this memory.
- Linux: Type
nvidia-smiin the terminal (shows your graphics card and its memory) andfree -hfor system memory.
Guide
Google Colab (in your browser, no installation)
Google Colab is a free service from Google. It gives you a computer with a graphics card in your browser for a few hours. All you need is a Google account. For this model: too big for Colab – even the largest Colab GPUs aren't enough.
- Download the notebook. Click the button and save the file.
- Open Colab. Go to colab.research.google.com and sign in with your Google account.
- Upload the notebook. In the window that appears, click Upload on the left (or choose File → Upload notebook at the top) and select the file
colab.ipynb. - Turn on the GPU. In the top menu, go to Runtime → Change runtime type → under Hardware accelerator, select T4 GPU (with Colab Pro: L4 or A100) → Save.
- Start everything. Runtime → Run all (keyboard shortcut Ctrl + F9). If you see the warning “This notebook was not authored by Google,” click Run anyway.
- Wait. The cells run one after another. A green checkmark appears to the left of each cell when it's done. Installation and download take about 126 minutes here.
- Try it out. The result appears below the last cell. To test something else, change the text in the cell and run it again with Shift + Enter. The last cell starts a proper chat with an input field.
- Clean up. When you're done: Runtime → Disconnect and delete runtime. This saves your free quota.
Good to know: The free GPU is limited (usually a few hours per day). After about 90 minutes of inactivity, Colab disconnects, and you'll have to start everything over.
Windows 10 / 11
The easiest way is Ollama, a free program that launches AI models with a single command.
- Update your graphics driver (NVIDIA cards only): Install the NVIDIA app or the latest driver. For AMD cards, use the latest Adrenalin driver.
- Install Ollama. Download the file OllamaSetup.exe from ollama.com/download/windows, double-click it, and choose Install. Afterward, a small llama icon appears in the taskbar at the bottom right.
- Open a terminal window. Press the Windows key, type PowerShell and press Enter. A blue or black window opens.
- Start the model. Copy this command ("Copy" button), paste it into the window with a right-click, and press Enter:
The first time, Ollama downloads the model (366 GB (about 8.1 hr at 100 Mbit/s)). After that, it's instant.ollama run hf.co/nazeshinjite/DeepSeek-V4.1-Flash-1017-ds4-GGUF:Q2 - Start typing. Once
>>> Send a messageappears, type your question and press Enter. Type/byeto end the chat.
Prefer automatic? A script that picks the right variant for you
- Download both files into the same folder: try_windows.ps1 demo.py
- Open the folder in File Explorer, right-click an empty spot and choose Open in Terminal.
- Paste this command and press Enter:
powershell -ExecutionPolicy Bypass -File try_windows.ps1
If needed, the script installs Ollama via winget, measures your VRAM (video memory) and downloads the matching variant.
macOS
Macs with an Apple chip (M1 to M5) are ideal. Older Intel Macs are very slow.
- Install Ollama. Download it from ollama.com/download/mac, open the file, and drag Ollama into your Applications folder. Launch Ollama once – a llama appears in the menu bar at the top.
- Open Terminal. Press Cmd + Space, type Terminal, and press Enter.
- Start the model. Paste the command (Cmd + V) and press Enter:
The first time, the model is downloaded (366 GB (about 8.1 hr at 100 Mbit/s)).ollama run hf.co/nazeshinjite/DeepSeek-V4.1-Flash-1017-ds4-GGUF:Q2 - Start typing. Type your question and press Enter. Quit with
/bye.
Linux
- Check your GPU.
nvidia-smishould show your card. If it doesn't, install the driver on Ubuntu and restart:sudo ubuntu-drivers install - Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh - Start the model:
Exit withollama run hf.co/nazeshinjite/DeepSeek-V4.1-Flash-1017-ds4-GGUF:Q2/bye. Useollama psto check whether the model is running on the GPU. - Or let it pick the right variant automatically:
bash try_linux.sh # mit Python/transformers statt Ollama: MODE=hf bash try_linux.sh
Try it
Write a Python script that merges all CSV files in a folder, and explain each step.Analyze this bar chart and summarize the three most important takeaways in English.Find the bug in this code and suggest a test that covers it.If something goes wrong
There is no Jinja chat template. Use the `encoding` folder or deepseek-recipe for the prompt format.
Per the instructions, you must convert the weights using the `inference` folder before running local inference.
The recommended settings are temperature 1.0 and top_p 1.0, not lower values.
The memory requirements are enormous, so check your hardware first.
“out of memory,” a crash, or your computer freezes
The model is too big for your VRAM (video memory). Pick a smaller variant from the table above and close other programs, especially games and browsers with lots of tabs.
It runs, but extremely slowly
Your CPU is probably doing the work instead of your graphics card. In Ollama, the PROCESSOR column of ollama ps shows how much runs on the GPU. If it says "100% CPU", update your graphics driver or pick a smaller variant that fits entirely into VRAM (video memory).
“ollama” or “python” is not found
Close the terminal window and open it again so it recognizes the newly installed program. For Python on Windows: reinstall it and check Add python.exe to PATH.
Windows: "Running scripts is disabled on this system"
Run the script with exactly the command above: powershell -ExecutionPolicy Bypass -File try_windows.ps1. This allows execution for this one time only.
Error message when loading the model (version, "trust_remote_code", unknown architecture)
Brand-new models often need the latest software version. With Ollama: update Ollama. With Python: pip install -U transformers in the activated environment. The Model Card usually lists the exact version.
Tokenstrom generates the scripts and notebook automatically. Please take a quick look before running them – they're plain, readable text. Mind the model's license: mit.
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