Ling-2.6-1T: A Trillion-Parameter Comprehensive Flagship Model for Complex Tasks

Today, we are thrilled to open-source Ling–2.6–1T from the Ling family.

Tailored for real–world, complex scenarios, this trillion–parameter model introduces targeted optimizations across inference efficiency, token overhead, and agentic capabilities, making it highly effective for coding and daily workflows.

Key upgrades in Ling–2.6–1T include:

• High Inference Efficiency: By adopting a hybrid architecture combining MLA and Linear Attention, we dramatically reduce latency and VRAM footprint for long contexts. It delivers superior throughput and lower per–token computational costs without sacrificing expressivity, ensuring real–time responsiveness for complex reasoning and tool calling.
• Lower Token Overhead via "Fast Thinking": We introduce a Contextual Process Redundancy Suppression reward strategy during post–training. This reduces reliance on verbose chains–of–thought (CoT), utilizing a "fast thinking" mechanism to reach answers directly and compress output costs while maintaining top–tier intelligence.
• Reliable Multi–Step Execution: With enhanced reasoning, agentic coding, and instruction following, Ling–2.6–1T achieves open–source SOTA on execution–heavy benchmarks, including AIME26, SWE–bench Verified, BFCL–V4, TAU2–Bench, and IFBench.
• Production–Ready for Agent Workflows: Designed for end–to–end engineering—from code generation to bug fixing—Ling–2.6–1T integrates seamlessly with mainstream agent frameworks like Claude Code, OpenClaw, OpenCode, and CodeBuddy, effortlessly handling multi–tool, multi–step constraints in enterprise environments.