DeepSeek-v4.1-Flash-1017
🚀 DeepSeek-V4.1-Flash-1017
👁️ Overview
The DeepSeek-V4.1-Flash-1017 represents a refined iteration of the DeepSeek-V4.1-Flash model. This update focuses on delivering significant leaps in several key domains:
- Reasoning & Coding: Enhanced logic and syntax capabilities.
- Agentic Workflows: Better performance in autonomous task execution.
- Visual Intelligence: Improved scores across multimodal benchmarks.
Beyond raw power, this version optimizes the performance–cost Pareto frontier. It achieves this by reducing the total length of generated outputs and decreasing the time required for task execution, effectively lowering the cost per request for the end-user.
📊 Model Specifications
Based on the repository metadata, the model utilizes a variety of precision formats to balance efficiency and accuracy.
| Attribute | Detail |
|---|---|
| Pipeline | image-text-to-text |
| Library | transformers / safetensors |
| Precision | 8-bit, fp8 |
| Region | 🇺🇸 US |
| License | MIT |
| Total Parameters |
Note on Parameters: The total parameter count is calculated as , with a significant portion stored in
F8_E4M3andI8formats.
📈 Performance Benchmarks
The following table compares DS-V4.1-Flash-1017 against its predecessor and other industry-leading models.
| 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 | 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 | 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 (tools) | 83.0 | 78.9 | 68.1 | — | 79.9 | 84.0 |
| BabyVision (tools) | 92.5 | 89.6 | 85.7 | — | 88.9 | 94.1 |
| ZeroBench-main (tools) | 51.0 | 49.0 | 41.0 | — | 53.0 | 52.0 |
† indicates results from the text-only subset of HLE.
Harnesses used: Claude Code for Terminal-Bench 4.0 and Mini-SWE-V2 for DeepSWE v1.1.
🛠️ Implementation & Encoding
Prompt Encoding Workflow
This release does not provide a Jinja-format chat template. Instead, it offers two paths for encoding prompts:
- Reference Implementation: The
encoding.pyfile in the encoding folder supports:- Multi-turn dialogues.
- Tool invocation and "thinking mode."
- Numeric reasoning effort levels.
- Interleaved images and mid-conversation system prompts.
- Production Toolkit: The
deepseek-recipe(Rust libraries with Python bindings) handles the conversion ofMessages,Chat Completions, andResponses APIrequests into the required format.
Local Inference
To get started with local deployment:
- Navigate to the
inferencefolder. - Follow the weight conversion guide.
- Configure the sampling parameters as shown below.
Recommended Sampling Settings:
{
"temperature": 1.0,
"top_p": 1.0,
"context_window": "1M tokens",
"max_tokens": ">= 256K"
}
📜 Legal & Contact
License: This model and its associated weights are released under the MIT License.
Citation:
@misc{deepseekai2026deepseekv41flash,
title={DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression},
author={DeepSeek-AI},
year={2026},
}
Support:
For inquiries or bug reports, please open an issue on the repository or email service@deepseek.com.
