Ling-2.6-1T - Ant Financial's open-source trillion-level integrated flagship model

What is Ling-2.6-1T?

Ling-2.6-1T is InclusionAI's latest open-source flagship model with trillions of parameters, designed specifically for agents, coding, and complex workflows. The model employs a hybrid architecture of MLA and Linear Attention, achieving strong comprehensive intelligence with extremely low token consumption. It achieves state-of-the-art (SOTA) performance in multiple execution benchmarks such as AIME26 and SWE-bench, emphasizing both "intelligence-efficiency ratio" and production-readiness.

Main functions of Ling-2.6-1T

• Complex task executionIt is designed for agent, coding, and automated office scenarios, and supports continuous task advancement such as planning, execution, correction, and verification.
• Code engineering capabilitiesIt covers a variety of development tasks, including code generation, bug fixing, and client/server/database development.
• Web page and design generationIt transforms style instructions into interactive front-end pages, supporting prototypes of various styles such as industrial, skeuomorphic, and data dashboards.
• Intelligent writing generationComplete various types of writing, including advertising copy, brand expression, and social media content, supporting multiple languages and cross-cultural contexts.
• Knowledge base constructionIt can accurately extract key knowledge points from massive documents, clarify complex entity relationships, and serve as an auxiliary tool for high-precision memory layers.
• Tool Calling and OrchestrationIt is highly compatible with mainstream agent frameworks and supports stable execution under multiple tools, multiple steps, and multiple constraints.

Technical Principles of Ling-2.6-1T

• Hybrid architecture of MLA and Linear AttentionIt integrates multi-head potential attention and linear attention mechanisms to reduce computational overhead while maintaining the upper limit of trillions of parameters.
• Reinforcement reward strategy to suppress process redundancyThe training strategy has undergone a deep evolution, avoiding meaningless semantic redundancy and improving information density and token efficiency.
• Evolutionary Mind Chain StrategyReduce reliance on lengthy thought processes, achieve results directly through efficient "fast thinking" mechanisms, and compress output costs at the same level of intelligence.
• Context redundancy detection mechanismIt actively identifies and filters redundant information when constructing logical paths, achieving high information density reasoning output.

How to use Ling-2.6-1T

• API calls: Obtain API keys through the Bailing Big Model Open Platform to connect to production systems or Agent frameworks.
• Coding Agent IntegrationIn coding agents such as OpenCode, you can directly call the model endpoint by configuring it to complete human-computer collaborative programming.
• Open source deploymentBased on open source weights, it can be deployed in local or private cloud environments, and is suitable for enterprise scenarios with high requirements for data security and independent control.
• Workflow EmbeddingBy combining long-term memory tools, knowledge base systems, and multiple toolchains, we can build automated workflows for complex business processes.

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

• Model NameLing-2.6-1T
• PublisherInclusionAI (a large-scale AI platform)
• Parameter magnitude1T (trillion-level)
• Open source statusIt has been officially open source.
• Model localizationA comprehensive flagship model designed for complex tasks, emphasizing intelligence efficiency, instruction execution, tool adaptation, and engineering implementation.
• How to useSupports API calls and can be embedded in Coding Agents such as OpenCode and mainstream Agent frameworks.

The core advantages of Ling-2.6-1T

• Extremely high intelligence efficiencyWith approximately 16M output tokens, the Intelligence Index reached approximately 34 points, entering a highly attractive range.
• Ultra-low token consumptionThe complete evaluation in Artificial Analysis used only 16M tokens, which is one of the lowest levels among similar models.
• Execution class benchmark open source SOTAIt achieves leading performance in AIME26, SWE-bench Verified, BFCL-V4, TAU2-Bench, IFBench, etc.
• Strong Agent AdaptabilityBoth Agentic Index and Coding Index are in the top tier, with stable tool calls and multi-step task progress.
• Long context and instruction complianceHigh scores in MRCR (16K-256K) and IFBench, maintaining logical consistency and execution accuracy under complex constraints.

Project address of Ling-2.6-1T

• HuggingFace model libraryhttps://huggingface.co/inclusionAI/Ling-2.6-1T

Comparison of Ling-2.6-1T with similar competing products

Application scenarios of Ling-2.6-1T

• Agent Automated WorkflowIt is responsible for long-term autonomous planning, high-frequency tool calls and multi-step business flow orchestration, and stable execution under complex constraints.
• Software Engineering DevelopmentCapable of performing collaborative programming tasks such as full-stack code generation, bug fixing, complex slide development, and game prototyping.
• Front-end and design prototypeIt can quickly transform industrial, skeuomorphic, and data dashboard style instructions into interactive and iterative landing pages and product prototypes.
• Professional content creationComplete diverse writing tasks, including advertising copy, brand narratives, cross-language content, and Subreddit-style posts, while maintaining a consistent style and natural expression.
• Enterprise knowledge managementIt accurately extracts key knowledge points from massive documents, clarifies complex entity relationships, and integrates them into long-term business systems as a high-precision memory layer.