Ling-3.1-flash

Introduction

We're introducing Ling-3.1-flash, an upgraded native hybrid reasoning model designed for complex, real-world tasks. With 560B total parameters, 25B active parameters per token, and a 1M-token context window, Ling-3.1-flash builds upon Ling-3.0-flash with improvements in architecture, performance on real-world capabilities.

With stronger real-world capabilities, Ling-3.1-flash shows improvements across a range of real-world tasks, including general-purpose agents, search, office productivity, and software engineering. It demonstrates strong performance on benchmarks such as GDPVal-AA V2.1, HealthBench Professional, FrontierSWE, and Terminal-Bench 4.0, covering office tasks, healthcare, software engineering, and terminal-based tool use. The model also shows improvements in specialized domains such as finance and materials science.

Model Overview

The model summary information and architecture diagram are as follows:

Architecture	Hybrid-linear MoE
Parameter Scale	Total 560B, Activated 25B
Transformer Layers	63 KDA + 9 Gated MLA (7:1)
Number of Dense Layers	4
Number of Routed Experts	512
Number of Shared Experts	1
Number of Activated Experts	8
Attention Heads	64
Hidden Size	4096
Expert Intermediate Size	1280
Dense Intermediate Size	10240
Vocabulary Size	157184

Evaluation

We have conducted a comprehensive evaluation of Ling-3.1-flash across multiple authoritative benchmarks. Ling-3.1-flash performs strongly on representative coding/agentic benchmarks including Terminal-Bench 4.0, SWE-bench Pro, FrontierSWE, GDPVal-AA V2.1, Automationbench, MCP-Atlas, SkillsBench, etc. In practice, Ling-3.1-flash delivers a strong user experience across frameworks including Claude Code,** Kilo Code**,** Qwen Code**,** Hermes Agent**,** and OpenClaw**, etc. Beyond agentic tasks, Ling-3.1-flash performs strongly on real-world tasks across STEM, healthcare, and finance.

We also introduce LingCode, a harness built on the open-source coding agent Pi, and enhanced to support complex, long-horizon coding tasks that run for hours, involve hundreds of rounds of tool calls, and require repeated context compaction. LingCode delivers significant performance gains on some coding agent tasks, and we are considering open-sourcing it in the future.