deepseek-ai/DeepSeek-V4.1-Flash-1017

Hugging Face
TEXT GENERATIONPricing:Input $0.225 / Cached $0.006 / Output $0.9Concurrent Unit Cost:4Model Size:763BQuant:FP8Context Size:256kPublished:Oct 17, 2026License:mitArchitecture:Transformer1.2K Open Weights Warm

DeepSeek-V4.1-Flash-1017 by deepseek-ai is a 763 billion parameter language model, an update to DeepSeek-V4.1-Flash, designed for enhanced performance in reasoning, coding, and agentic tasks. It features significant improvements across various benchmarks, including HLE, Terminal-Bench, and DeepSWE, while also reducing task execution time and output length. This model is optimized for faster task completion and lower operational costs, pushing the performance-cost frontier for agent harnesses.

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DeepSeek-V4.1-Flash-1017 Overview

DeepSeek-V4.1-Flash-1017 is an updated version of the DeepSeek-V4.1-Flash model by deepseek-ai, focusing on substantial performance gains across critical AI domains. This iteration delivers notable improvements in reasoning, coding, and agentic capabilities, alongside better visual benchmark scores. A key differentiator is its optimization for efficiency, reducing both task execution time and output length, which translates to faster task completion and lower operational costs for users.

Key Capabilities

  • Enhanced Reasoning: Demonstrates significant improvements on benchmarks like HLE (42.6) and Agents’ Last Exam (35.4).
  • Superior Coding: Achieves higher scores in coding-related tasks, including Terminal-Bench 4.0 (42.3), DeepSWE v1.1 (77.0), and ProgramBench (29.1).
  • Agentic Task Performance: Shows strong performance in agent-based scenarios, with scores like 60.5 on Automation-Bench v1.0.6 and 72.1 on Apex Bench v1.1.
  • Visual Understanding: Excels in visual benchmarks with tools, scoring 83.0 on Chartography and 92.5 on BabyVision.
  • Cost-Efficiency: Designed to reduce task execution time and output length, optimizing the performance-cost trade-off for agentic workflows.

Good for

  • Applications requiring advanced reasoning and problem-solving.
  • Code generation, debugging, and software engineering tasks.
  • Developing and deploying AI agents that need to perform complex actions efficiently.
  • Tasks involving visual data interpretation and analysis with tool integration.
  • Users looking to optimize operational costs and execution speed for AI workloads.

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