Ling-1T

Introduction

Ling-1T is the first flagship non-thinking model in the Ling 2.0 series, featuring 1 trillion total parameters with ≈ 50 billion active parameters per token. Built on the Ling 2.0 architecture, Ling-1T is designed to push the limits of efficient reasoning and scalable cognition.

Pre-trained on 20 trillion+ high-quality, reasoning-dense tokens, Ling-1T-base supports up to 128K context length and adopts an evolutionary chain-of-thought (Evo-CoT) process across mid-training and post-training. This curriculum greatly enhances the model’s efficiency and reasoning depth, allowing Ling-1T to achieve state-of-the-art performance on multiple complex reasoning benchmarks—balancing accuracy and efficiency.

Flagship-Level Efficient Reasoning

We comprehensively evaluated Ling-1T against leading flagship models, including both open-source giants (e.g., DeepSeek-V3.1-Terminus, Kimi-K2-Instruct-0905) and closed-source APIs (GPT-5-main, Gemini-2.5-Pro). Across code generation, software development, competition-level mathematics, professional math, and logical reasoning, Ling-1T consistently demonstrates superior complex reasoning ability and overall advantage.

In the AIME 25 benchmark, Ling-1T extends the Pareto frontier of reasoning accuracy vs. reasoning length, showcasing its strength in “efficient thinking and precise reasoning.”

Aesthetic Understanding and Front-End Generation

Ling-1T excels in visual reasoning and front-end code generation tasks, combining deep semantic understanding with precise code synthesis. We introduce a hybrid Syntax–Function–Aesthetics reward mechanism, enabling the model to not only generate correct and functional code but also demonstrate a refined sense of visual aesthetics. On ArtifactsBench, Ling-1T ranks first among open-source models, and the benchmark visualizations in this card were, in fact, generated by Ling-1T itself.

Emergent Intelligence at Trillion-Scale

Scaling to the trillion-parameter level has revealed strong emergent reasoning and transfer capabilities. For example, in the BFCL V3 tool-use benchmark, Ling-1T achieves ≈ 70% tool-call accuracy with only light instruction tuning—despite having seen no large-scale trajectory data during training. Ling-1T can:

• Interpret complex natural-language instructions
• Transform abstract logic into functional visual components
• Generate cross-platform compatible front-end code
• Create stylistically controlled marketing copy and multi-lingual text

These capabilities form the foundation for general, collaborative human–AI intelligence, which we aim to advance together with the open-source community through Ling-1T’s release.

Pre-Training at Trillion Scale

The Ling 2.0 architecture was designed from the ground up for trillion-scale efficiency, guided by the Ling Scaling Law (arXiv:2507.17702). This ensures architectural and hyperparameter scalability even under 1e25–1e26 FLOPs of compute.

Key architectural innovations include:

• 1T total / 50B active parameters with a 1/32 MoE activation ratio
• MTP layers for enhanced compositional reasoning
• Aux-loss-free, sigmoid-scoring expert routing with zero-mean updates
• QK Normalization for fully stable convergence

Ling-1T is the largest FP8-trained foundation model known to date. FP8 mixed-precision training yields 15%+ end-to-end speedup, improved memory efficiency, and maintains ≤ 0.1% loss deviation from BF16 across 1T tokens. A fine-grained, heterogeneous 1F1B interleaved pipeline further boosts utilization by 40 %+. System-level optimizations—fused kernels, communication scheduling, recomputation, checkpointing, simulation, and telemetry—ensure stable trillion-scale training.

Pre-training used over 20T high-quality tokens, with > 40% reasoning-dense data in later stages. Mid-training introduced curated chain-of-thought corpora for “reasoning pre-activation”, improving downstream reasoning stability. A custom WSM (Warmup–Stable–Merge) LR scheduler（arXiv:2507.17634） with mid-train checkpoint merging simulates LR decay and boosts generalization.

Post-Training and Evo-CoT Optimization

Built upon mid-training reasoning activation, post-training adopts Evo-CoT (Evolutionary Chain-of-Thought) for progressive reasoning enhancement under controllable cost. This approach continually expands the Pareto frontier of reasoning accuracy vs. efficiency—ideal for reflexive non-thinking models.

For reinforcement learning, we introduce LPO (Linguistics-Unit Policy Optimization) —a novel sentence-level policy optimization method. Unlike GRPO (token-level) or GSPO (sequence-level) algorithms, LPO treats sentences as the natural semantic action units, enabling precise alignment between rewards and reasoning behavior. Empirically, LPO offers superior training stability and generalization across reasoning tasks.

Evaluation

Ling-1T has been extensively evaluated across knowledge, code, math, reasoning, agent, and alignment benchmarks. It currently stands as the best open-source flagship non-thinking model, rivaling closed-source APIs in complex reasoning while maintaining exceptional efficiency and interpretability.

Model Downloads

You can download Ling-1T from the following table. If you are located in mainland China, we also provide the model on ModelScope.cn to speed up the download process.

| Model | Context Length | Download | | :-------: | :------------