Ling-1T : The best open-sourced LLM
Ling-1T beats Kimi-K2, DeepSeek-v3
Ling-1T, A one-trillion-parameter model that now takes the crown of the most powerful open-source LLM built so far. It’s not just another big model release , it’s an engineering statement.
Ling-1T is the first flagship non-thinking model from the Ling 2.0 series, a term the creators use to describe models that don’t “hallucinate” abstractly but reason efficiently through structure.
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The Non-Thinking Flagship
Ling-1T runs on the Ling 2.0 architecture with 1 trillion total parameters, but only 50 billion active parameters per token that’s a sparse activation setup, something like a MoE (Mixture of Experts) but optimized for efficiency. This is the same idea behind DeepSeek’s hybrid MoE routes, but Ling-1T’s design seems cleaner, with stronger convergence and lower compute waste.
The model was trained on 20 trillion+ reasoning-heavy tokens, not just raw web dumps. It supports a 128K context length, making it suitable for complex reasoning chains, long document analysis, and multi-turn logic-heavy tasks.
A special piece in its design is the Evolutionary Chain-of-Thought (Evo-CoT) method. It’s not just prompting tricks, it’s a process baked into training that evolves reasoning ability mid-way and post-training, letting the model “think” efficiently without redundant token expansion.
Efficient Reasoning that Rivals Closed Models
Ling-1T was benchmarked against both open and closed giants: DeepSeek-V3.1, Kimi-K2, GPT-5, Gemini 2.5. Across code generation, competition-level math, and symbolic reasoning.
On AIME 25, a high-level reasoning benchmark, it extended the Pareto frontier meaning it delivered higher reasoning accuracy while keeping token usage low. That’s efficiency in thought, not brute force.
If you’ve used GPT-5 API or DeepSeek-R1, you know these models sometimes “overspeak” their reasoning. Ling-1T’s results suggest it’s tighter, faster, and more direct like a coder who doesn’t comment everything, but nails every logic branch.
Visual Intelligence and Front-End Code
Unlike most reasoning-focused models, Ling-1T also has strong visual and front-end generation capabilities. It uses something called Syntax–Function–Aesthetics (SFA) rewards, meaning it doesn’t just generate correct code, but visually pleasing, human-readable code that aligns with design aesthetics.
On ArtifactsBench, a benchmark for front-end generation and UI reasoning, Ling-1T ranked first among open models. And all the visual examples on its release card were generated by Ling-1T itself.
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Trillion-Scale Intelligence and Transfer
Once you cross the trillion-parameter threshold, weird things start to happen, emergent behaviors. Ling-1T shows this too.
Even without large-scale tool-use data, it hits ~70% tool-call accuracy on the BFCL V3 benchmark. That means it can correctly choose and use functions or APIs from natural-language instructions.
It can:
- Interpret complex natural instructions
- Convert logic into visual or code components
- Write cross-platform UI code
- Generate multi-lingual, stylistically coherent text
That last part multi-lingual reasoning is often where open models fall short. Ling-1T seems to have cracked a part of it.
The Trillion-Scale Training Stack
The hardware story behind this is equally wild. Ling-1T runs a 1T total / 50B active parameter model under a 1/32 MoE ratio. The compute efficiency follows what they call the Ling Scaling Law (arXiv:2507.17702) basically, how to scale performance without waste.
Some of the tricks:
- QK Normalization for stable convergence
- Sigmoid-scoring expert routing without auxiliary loss
- FP8 mixed precision, which gave a 15%+ speedup compared to BF16
- 1F1B interleaved pipelines for +40% utilization
Training used 20T+ reasoning-dense tokens, 40% of which were high-quality CoT sequences. The WSM scheduler (Warmup–Stable–Merge) merges checkpoints mid-training almost like model “evolution” built into the loop.
Post-Training: The Evo-CoT Era
The Evo-CoT optimization continues refining reasoning after pre-training. It expands the accuracy–efficiency trade-off frontier, helping Ling-1T reason more deeply with fewer steps.
Then there’s LPO (Linguistics-Unit Policy Optimization), a reinforcement learning technique that optimizes at the sentence level instead of per-token (like GRPO or GSPO). That’s important. Because human thought doesn’t happen per token either, it happens per idea or sentence.
Evaluation and Performance
Ling-1T topped or matched closed models in most reasoning and code benchmarks while staying efficient enough to be run in open environments like vLLM and SGLang.
It’s already supported for FP8 and BF16 inference, and even long-context reasoning using YaRN scaling. So technically, anyone with serious hardware could deploy it.
The Limitations
Ling-1T’s not perfect.
- GQA-based attention still costs more compute than ideal.
- Agentic ability it’s not yet great at long-term dialogue or memory.
- Sometimes it breaks role or instruction identity, similar to early GPT-4 behaviors.
The team says future versions will tackle hybrid attention, improved alignment, and better long-memory design.
Final Thoughts
Ling-1T feels like the moment open-source finally matches closed APIs in reasoning depth, not just size. It’s a trillion-parameter model that actually uses its scale efficiently.
Most big releases are noisy, overhyped. But this one’s quietly serious: the first non-thinking model that actually thinks efficiently. The kind that doesn’t just chase benchmarks, it redefines what “efficient intelligence” looks like.
Ling-1T : The best open-sourced LLM was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
