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deepseek-ai/DeepSeek-V4.1-Flash
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Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators)
Original repository: deepseek-ai/DeepSeek-V4.1-Flash-1017
Released GGUF repository: mrfakename/DeepSeek-V4.1-Flash-1017-GGUF
DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.
| Benchmark | DS-V4.1-Flash-1017 | DS-V4.1-Flash | K3 | GLM-5.3 | GPT-5.6 Sol | Opus 5 |
|---|---|---|---|---|---|---|
| HLE w/o tools | 42.6 (44.0†) | 36.8 (39.1†) | 43.5 | 42.0† | 44.5 | 56.3 |
| Terminal-Bench 4.0 | 42.3 | 31.2 | 12.6 | 37.9 | 39.9 |
† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.
This release does not include a Jinja-format chat template. The encoding folder contains a self-contained Python reference implementation (encoding.py) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content.
For production use, we additionally release deepseek-recipe, a set of Rust libraries with Python bindings that provides the same prompt format as a maintained, protocol-aware toolkit. It converts Messages, Chat Completions, and Responses API requests into the Conversation format, encodes them into DeepSeek V4 and V4.1 prompts or token IDs, and parses model output back into complete or streamed responses — covering thinking, tool calls, images, and generation settings. Model inference, tool execution, and HTTP transport are left to the caller.
Please refer to the inference folder for instructions on weight conversion and running inference locally.
Recommended sampling parameters:
| Parameter | Value |
|---|---|
temperature | 1.0 |
top_p | 1.0 |
context_window | 1M tokens |
max_tokens | ≥ 256K |
This repository and the model weights are licensed under the MIT License.
bibtex
@misc{deepseekai2026deepseekv41flash, title={DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression}, author={DeepSeek-AI}, year={2026},}GGUF quants of deepseek-ai/DeepSeek-V4.1-Flash-1017.
| Folder | Size | Files | Notes |
|---|---|---|---|
Q4_K_M | 414 GiB | 10 | The routed experts are already about 4-bit, so this saves less than usual. |
Q2_K | 246 GiB | 10 | Smallest usable size. |
These are plain quants with no imatrix. Each folder holds a split GGUF. Point llama.cpp at the -00001-of- file and it loads the rest.
Mainline llama.cpp doesn't support DeepSeek V4.1 yet.
These files were made with the branch from ggml-org/llama.cpp#28696 at commit 2076412, and they need that branch or a build that includes it.
text
git clone https://github.com/vcruz305/llama.cpp && cd llama.cppgit checkout 2076412598e99127ea2554467b2b7530856895d3cmake -B build && cmake --build build -jThe two engram tables are large and read a few rows per token. Keeping them on the CPU works well, for example -ot "engram_embd.weight=CPU". The PR has a tested command line.
Text only for now. The llama.cpp branch can't convert the vision tower yet.
Huge thanks to Hugging Face for generously sponsoring the compute used for this project 🤗
If you have any questions, please raise an issue or contact us at service@deepseek.com.
| 51.8 |
| DeepSWE v1.1 | 77.0 | 74.2 | 67.5 | 66.9 | 73.0 | 74.0 |
| ProgramBench | 29.1 | 20.3 | 17.5 | 19.0 | 23.0 | 37.0 |
| NL2Repo-Bench | 68.2 | 65.4 | 58.0 | 58.0 | 56.8 | 75.3 |
| Automation-Bench v1.0.6 | 60.5 | 54.8 | 46.7 | 48.8 | 45.8 | 50.3 |
| Agents’ Last Exam | 35.4 | 31.8 | 27.6 | 28.5 | 26.7 | 28.6 |
| Apex Bench v1.1 | 72.1 | 64.6 | 47.8 | 56.6 | 54.3 | 65.8 |
| Chartography (w/tools) | 83.0 | 78.9 | 68.1 | — | 79.9 | 84.0 |
| BabyVision (w/tools) | 92.5 | 89.6 | 85.7 | — | 88.9 | 94.1 |
| ZeroBench-main (w/tools) | 51.0 | 49.0 | 41.0 | — | 53.0 | 52.0 |