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DeepSeek-V4.1-Flash-1017

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LLMs#7AI Models & LLMsOpen source · Web

DeepSeek-V4.1-Flash-1017 is an updated version of the DeepSeek model, enhancing reasoning, coding, and visual benchmarks. It reduces task execution time and output length, offering faster and cost-effective solutions for users.

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Description

DeepSeek-V4.1-Flash-1017 is a minor update to the DeepSeek-V4.1-Flash model, providing significant improvements in reasoning, coding, agentic tasks, and visual benchmarks. This model is designed to enhance performance while reducing task execution time and output length, allowing users to complete tasks more efficiently and at a lower cost. The model advances the performance-cost Pareto frontier across multiple agent harnesses, making it a valuable tool for various applications.

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The model has been benchmarked against several others, showing notable improvements in areas such as HLE without tools, Terminal-Bench 4.0, DeepSWE v1.1, and more. For instance, in the HLE benchmark without tools, DeepSeek-V4.1-Flash-1017 scored 42.6 compared to its predecessor's 36.8, demonstrating its enhanced capabilities.

DeepSeek-V4.1-Flash-1017 includes a self-contained Python reference implementation for prompt encoding, supporting multi-turn conversations, tool calling, thinking mode, numeric reasoning, and more. Additionally, it offers a set of Rust libraries with Python bindings, known as deepseek-recipe, which provides a maintained, protocol-aware toolkit for converting API requests into the Conversation format and parsing model output.

The model is licensed under the MIT License, ensuring open access and flexibility for users. For those interested in running inference locally, instructions are available in the inference folder, detailing weight conversion and recommended sampling parameters.

  • CategoryAI Models & LLMs
  • Pricing ModelOpen source
  • DeploymentCloud, Self-hosted
  • PlatformsWeb

DeepSeek-V4.1-Flash-1017 Highlights

  • Enhanced reasoning capabilities

  • Improved coding performance

  • Faster task execution

  • Reduced output length

  • Advanced visual benchmarks

  • Python reference implementation

  • Rust libraries with Python bindings

  • Open-source under MIT License

Getting Started with DeepSeek-V4.1-Flash-1017

  1. Access model page: Visit the Hugging Face model page

  2. Authenticate: Log in to your Hugging Face account

  3. Configure: Set up the model with recommended parameters

  4. Send prompts: Use the model for various tasks

  5. Fine-tune: Adjust the model for specific needs

DeepSeek-V4.1-Flash-1017's Use Cases

  • Reasoning Enhancement
  • Coding Efficiency
  • Visual Benchmarking
  • Cost Reduction
  • Open Source Development

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