1T
262,144 tokens
MIT
Official open-weight trillion-parameter Ling flagship for complex coding, multi-step execution, and agentic workflows. The Hugging Face model card describes Ling-2.6-1T as a comprehensive flagship model optimized for real-world complex scenarios, using a hybrid MLA plus Linear Attention architecture to reduce long-context latency and VRAM costs while improving throughput. InclusionAI highlights a post-training 'Fast Thinking' strategy based on Contextual Process Redundancy Suppression to reduce verbose chain-of-thought overhead and lower output-token costs while maintaining high capability. Release materials claim open-source state-of-the-art performance on execution-heavy benchmarks including AIME26, SWE-bench Verified, BFCL-V4, TAU2-Bench, and IFBench, with strong long-context performance on MRCR from 16K to 256K. The model is positioned as production-ready for engineering and enterprise agent workflows, with explicit compatibility claims for frameworks such as Claude Code, OpenClaw, OpenCode, and CodeBuddy, plus deployment guidance for both standard inference and MTP-accelerated serving in SGLang and vLLM.