Ling-3.0-flash-fp4

About

What Ling-3.0-flash-fp4 is

Introduction We're introducing Ling-3.0-flash, our next-generation native hybrid reasoning model. Operating with 124B total and 5.1B active parameters (~12.4% and ~8.1% of our previous 1T-class flagship Ring-2.6-1T), Ling-3.0-flash matches or outperforms its predecessor across key benchmarks. Key highlights of the model are summarized below: + Native Hybrid-Linear Architecture: Ling-3.0 adopts a native hybrid linear attention architecture from the very start of pretraining (5:1 alternating stacking of Kimi Delta Attention (KDA) and MLA), upgraded with KDA fine-grained diagonal gating and 1/64 sparse MoE. With 124B total parameters and 5.1B activated parameters, it achieves a synergistic leap in long-context efficiency and computational cost. + Remarkable Efficiency & Performance: Engineered for speed, compute efficiency, and production deployment, Ling-3.0-flash delivers class-defying performance against both larger SOTA competitors and previous-generation flagships. Activating only 5.1B parameters per token, it provides impressive reasoning, instruction following, and long-context capabilities to empower complex agentic workflows in production environments. + Comprehensive Agentic Evolution: Tailored for real-world productivity workflows, the model incorporates 10,000+ interactive training environments to achieve end-to-end closed-loop execution across Coding, General, and Deep Research Agent tasks. It natively integrates the SGLang HiCache + Mooncake hierarchical caching architecture (featuring physical dual-pools and a cluster-shared L3 cache), eliminating redundant recomputation during long-horizon interactions and reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios. Model Overview The model summary information and architecture diagram are as follows: Evaluation We have conducted a comprehensive evaluation of Ling-3.0-flash across multiple authoritative benchmarks. Ling-3.0-flash performs strongly on representative code/agent benchmarks such as SWE-Bench Pro, SWE-Bench Multilingual, Tau3-banking-AA, MCP-Atlas and SkillsBench, etc. In practice, Ling-3.0-flash delivers a strong user experience across frameworks including Claude Code,

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys ling-3-0-flash-fp4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ling-3-0-flash-fp4 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ling-3-0-flash-fp4","messages":[{"role":"user","content":"Hello"}]}'

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