DeepSeek-AI has released DeepSeek-V4.1-Flash-1017, described as a minor update to its DeepSeek-V4.1-Flash model. According to the model documentation, the update delivers “substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks” while reducing task execution time and output length.
The model is available on Hugging Face and can be run using several inference frameworks. Developers can use it with Transformers, vLLM, or SGLang, with instructions provided for local deployment, Docker containerization, and cloud notebook environments like Google Colab and Kaggle.
Benchmark results show improvements across multiple evaluation harnesses. On the HLE benchmark (without tools), DeepSeek-V4.1-Flash-1017 scored 42.6, up from 36.8 for the previous Flash version. On Terminal-Bench 4.0, it achieved 42.3 compared to 31.2. The model also improved on coding-related benchmarks: DeepSWE v1.1 reached 77.0 (from 74.2), ProgramBench scored 29.1 (from 20.3), and NL2Repo-Bench achieved 68.2 (from 65.4). For agentic tasks, Automation-Bench v1.0.6 showed 60.5 compared to 54.8.
On visual benchmarks, BabyVision (with tools) reached 92.5, up from 89.6, and Chartography (with tools) scored 83.0 versus 78.9.
The model supports a 1 million token context window and recommends a maximum of 256K tokens for generation. Recommended sampling parameters include a temperature of 1.0 and top_p of 1.0. DeepSeek provides a Python reference implementation for prompt encoding in the model repository, along with a maintained Rust toolkit called deepseek-recipe for production use.
The model is licensed under the MIT License. Installation and usage instructions are available through Hugging Face, with deployment options ranging from simple Python pipelines to containerized setups with GPU support.
Key facts
- DeepSeek-V4.1-Flash-1017 shows improvements in reasoning, coding, agentic tasks, and visual benchmarks compared to the previous Flash version
- The model reduces task execution time and output length, aiming to lower costs
- It supports a 1 million token context window and up to 256K tokens for generation
- The model can be deployed using Transformers, vLLM, SGLang, or Docker
- DeepSeek provides both a Python reference implementation and a maintained Rust toolkit for prompt encoding