Quick Brief
Ant Group's Inclusion AI has officially released the weights for its flagship large language model, Ling-2.6-1T. This trillion-parameter MoE...
Architecture and Core Features
Ling-2.6-1T shares the Same advanced architecture as the recently open-sourced Lightweight "flash" version (104B total / 7.4B active parameters). It employs a hybrid attention mechanism combining MLA (Multi-head Latent Attention) and Lightning Linear Attention. A key innovation in the 1T version is the introduction of a "Fast-Thinking" training strategy. By incorporating a "Contextual Process Redundancy Suppression" reward during post-training, the model is optimized to compress verbose chain-of-thought ouTPUts. This APProach significantly reduces Token Consumption without compromising the quality of reasoning or task execution. The model dEMOnstrates state-of-the-art performance among open-source non-reasoning models on several complex task benchmarks: SWE-bench Verified: Achieved a score of 72.2%, a notable improvement over the flash version's 61.2%. For context, some open-source reasoning models like DeepSeek V4 Pro have surpassed 80%.
Other Benchmarks: The model reportedly reaches top-tier performance on execution-focused tests including AIME 2026, BFCL-V4, TAU2-Bench, and IFBench.
Efficiency: On the Artificial Analysis comprehensive Intelligence index, the model scored 34, consuming approximately 16 million output tokens for the full evaluation, highlighting its Token Efficiency.
API Access: A free API is currently available via OpenRouter.
Self-Hosting: For developers looking to self-host, deployment requires a minimum of 8 GPUs. The model supports popular inference engines like SGLang and vLLM.
Be the first to rate this article.
Comments & Questions (0)
No comments yet
Be the first to comment!