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Ling-3.0-flash

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LLMsAI Models & LLMsFreemium · Web

Ling-3.0-flash is a next-generation hybrid reasoning model designed for efficiency and performance. With 124 billion parameters, it excels in reasoning, instruction following, and long-context understanding, making it ideal for complex workflows.

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Description

Ling-3.0-flash is a cutting-edge hybrid reasoning model developed by inclusionAI and hosted on Hugging Face. It operates with a total of 124 billion parameters, with 5.1 billion activated per token, providing remarkable efficiency and performance. The model adopts a native hybrid linear attention architecture, featuring Kimi Delta Attention (KDA) and MLA, upgraded with fine-grained diagonal gating and sparse MoE. This architecture allows Ling-3.0-flash to achieve a synergistic leap in long-context efficiency and computational cost.

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Engineered for speed and compute efficiency, Ling-3.0-flash delivers class-defying performance against larger state-of-the-art competitors and previous-generation flagships. It empowers complex agentic workflows in production environments, activating only 5.1 billion parameters per token. The model incorporates 10,000+ interactive training environments to achieve end-to-end closed-loop execution across coding, general, and deep research agent tasks.

Ling-3.0-flash integrates the SGLang HiCache + Mooncake hierarchical caching architecture, featuring physical dual-pools and a cluster-shared L3 cache. This setup eliminates redundant recomputation during long-horizon interactions, reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios. The model performs strongly across multiple authoritative benchmarks, including SWE-Bench Pro, SWE-Bench Multilingual, Tau3-banking-AA, MCP-Atlas, and SkillsBench.

The model is tailored for real-world productivity workflows, delivering strong performance in general knowledge, mathematical reasoning, instruction following, and long-context understanding. Ling-3.0-flash is designed for production deployment, offering impressive reasoning capabilities and long-context efficiency, making it suitable for complex agentic workflows.

  • CategoryAI Models & LLMs
  • Pricing ModelFreemium
  • DeploymentCloud
  • PlatformsWeb

Ling-3.0-flash Highlights

  • Native Hybrid-Linear Architecture

  • Remarkable Efficiency & Performance

  • Comprehensive Agentic Evolution

  • SGLang HiCache + Mooncake Caching

  • End-to-End Closed-Loop Execution

  • Long-Context Efficiency

  • Instruction Following

  • Mathematical Reasoning

Getting Started with Ling-3.0-flash

  1. Access model page: Visit Hugging Face

  2. Authenticate: Log in to your account

  3. Configure: Set parameters and preferences

  4. Send prompts: Input your queries

  5. Fine-tune: Adjust model settings

Ling-3.0-flash's Use Cases

  • Coding Tasks
  • Research Agents
  • Instruction Following
  • Long-Context Understanding
  • Mathematical Reasoning

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