Ling-2.6-1T - Ant Ling's open-source trillion-scale comprehensive flagship model

What is Ling-2.6-1T

Ling-2.6-1T is the latest open-source trillion-parameter comprehensive flagship model from the Ling large model (InclusionAI), designed specifically for Agent, Coding, and complex workflows. The model adopts a hybrid architecture of MLA and Linear Attention, achieving strong comprehensive intelligence with extremely low Token consumption, reaching open-source SOTA on multiple execution-oriented benchmarks such as AIME26 and SWE-bench, emphasizing "intelligence-efficiency ratio" and deployability in production environments.

Main features of Ling-2.6-1T

• Complex task execution: oriented toward Agent, Coding, and automated office scenarios, supporting continuous task advancement such as planning, execution, correction, and verification.
• Code engineering capability: covering diverse development tasks such as code generation, defect repair, and client/server/database development.
• Webpage and design generation: converting style instructions into interactive front-end pages, supporting prototypes in multiple styles such as industrial style, skeuomorphism, and data dashboards.
• Intelligent writing generation: completing multiple types of writing such as advertising copy, brand expression, and social media content, supporting multilingual and cross-cultural contexts.
• Knowledge base construction: accurately extracting key knowledge points from massive documents, clarifying complex entity relationships, and assisting work as a high-precision memory layer.
• Tool calling and orchestration: highly compatible with mainstream Agent frameworks, supporting stable execution in multi-tool, multi-step, and multi-constraint environments.

Technical principles of Ling-2.6-1T

• Hybrid architecture of MLA and Linear Attention: integrating multi-head latent attention and linear attention mechanisms, reducing computational overhead while maintaining the upper limit of trillion-parameter capability.
• Reinforcement reward strategy for suppressing process redundancy: deep evolution of training strategy, avoiding meaningless semantic redundancy, and improving information density and Token efficiency.
• Evolving chain-of-thought strategy: reducing dependence on lengthy chains of thought, directly reaching results with an efficient "fast thinking" mechanism, and compressing output cost at the same level of intelligence.
• Context redundancy judgment mechanism: proactively identifying and filtering redundant information when constructing logical paths, achieving reasoning output with high information density.

How to use Ling-2.6-1T

• API calling: obtaining an API key through the Ling large model open platform, and integrating it into production systems or Agent frameworks.
• Coding Agent integration: in OpenCode and other coding agents, directly calling by configuring the model endpoint to complete human-machine collaborative programming.
• Open-source deployment: deploying based on open-source weights in local or private cloud environments, suitable for enterprise scenarios with high requirements for data security and autonomous control.
• Workflow embedding: combining long-term memory tools, knowledge base systems, and multi-tool chains to build complex business automation workflows.

Key information and usage requirements of Ling-2.6-1T

• Model name: Ling-2.6-1T (Ling-2.6-1T)
• Publisher: Ling large model (InclusionAI)
• Parameter scale: 1T (trillion-scale)
• Open-source status: officially open-sourced
• Model positioning: A comprehensive flagship model for complex tasks, emphasizing intelligence efficiency, instruction execution, tool adaptation, and engineering implementation
• Usage: Supports API calls and can be embedded in Coding Agents such as OpenCode and mainstream Agent frameworks

Core Advantages of Ling-2.6-1T

• Extremely High Intelligence Efficiency: Achieves an Intelligence Index of about 34 with approximately 16M output tokens, entering a highly attractive range.
• Ultra-Low Token Consumption: Uses only 16M tokens in the complete Artificial Analysis evaluation, one of the lowest levels among comparable models.
• Open-Source SOTA on Execution Benchmarks: Achieves leading performance on AIME26, SWE-bench Verified, BFCL-V4, TAU2-Bench, IFBench, and others.
• Strong Agent Adaptability: Both Agentic Index and Coding Index are in the first tier, with stable tool calling and multi-step task advancement.
• Long Context and Instruction Following: High scores on MRCR (16K-256K) and IFBench, maintaining logical consistency and execution accuracy under complex constraints.

Ling-2.6-1T Project Address

• HuggingFace Model Repository：https://huggingface.co/inclusionAI/Ling-2.6-1T

Comparison of Ling-2.6-1T with Similar Competitors

Application Scenarios of Ling-2.6-1T

• Agent Automated Workflows: Handles long-horizon autonomous planning, high-frequency tool calling, and multi-step business process orchestration, steadily advancing execution under complex constraint environments.
• Software Engineering Development: Capable of human-AI collaborative programming tasks such as full-stack code generation, defect fixing, complex Slide development, and game prototype construction.
• Front-End and Design Prototypes: Rapidly converts style instructions such as industrial style, skeuomorphism, and data dashboards into interactive, iterable Landing Pages and product prototypes.
• Professional Content Creation: Completes diverse writing such as advertising copy, brand narratives, cross-language content, and Subreddit-style posts, maintaining stable style and natural expression.
• Enterprise Knowledge Management: Accurately distill key knowledge points from massive documents, clarify complex entity relationships, and serve as a high-precision memory layer integrated into long-term business systems.