--- title: Ling-2.6-1T canonical_url: "https://www.modelscope.cn/models/inclusionAI/Ling-2.6-1T" md_url: "https://www.modelscope.cn/models/inclusionAI/Ling-2.6-1T.md" repository: inclusionAI/Ling-2.6-1T chinese_name: Ling-2.6-1T last_updated: 2026-09-24 license: mit pipeline_tag: text-generation tasks: - text-generation model_type: - bailing_hybrid architectures: - BailingMoeV2_5ForCausalLM parameters: 1025.4B tensor_type: - F8_E4M3 - F32 - BF16 library_name: - safetensors supports_inference: txt2txt downloads: 14199 stars: 30 --- # Ling-2.6-1T > Ling-2.6-1T - inclusionAI 在 ModelScope 开源的模型。🤗 Hugging Face    |   🤖 ModelScope    |   🐙 OpenRouter    |    Tech Report inclusionAI/Ling-2.6-1T 是 ModelScope 魔搭社区上的 1025.4B 参数text-generation模型，采用 mit 许可，并支持在线推理（txt2txt）。 - **Repository**: inclusionAI/Ling-2.6-1T - **License**: mit - **Tasks**: text-generation - **Parameters**: 1025.4B - **Online inference**: txt2txt - **Downloads**: 14199 - **Stars**: 30 - **Last updated**: 2026-09-24 Source: https://www.modelscope.cn/models/inclusionAI/Ling-2.6-1T ---

🤗 Hugging Face   |   🤖 ModelScope    |   🐙 OpenRouter    |    Tech Report

## Ling-2.6-1T: A Trillion-Parameter Comprehensive Flagship Model for Complex Tasks Today, we are thrilled to open-source **Ling–2.6–1T** from the Ling family. Tailored for real–world, complex scenarios, this trillion–parameter model introduces targeted optimizations across inference efficiency, token overhead, and agentic capabilities, making it highly effective for **coding and daily workflows**. Key upgrades in **Ling–2.6–1T** include: * **High Inference Efficiency:** By adopting a hybrid architecture combining **MLA and Linear Attention**, we dramatically reduce latency and VRAM footprint for long contexts. It delivers superior throughput and lower per–token computational costs without sacrificing expressivity, ensuring real–time responsiveness for complex reasoning and tool calling. * **Lower Token Overhead via "Fast Thinking":** We introduce a *Contextual Process Redundancy Suppression* reward strategy during post–training. This reduces reliance on verbose chains–of–thought (CoT), utilizing a "fast thinking" mechanism to reach answers directly and compress output costs while maintaining top–tier intelligence. * **Reliable Multi–Step Execution:** With enhanced reasoning, agentic coding, and instruction following, Ling–2.6–1T achieves **open–source SOTA** on execution–heavy benchmarks, including AIME26, SWE–bench Verified, BFCL–V4, TAU2–Bench, and IFBench. * **Production–Ready for Agent Workflows:** Designed for end–to–end engineering—from code generation to bug fixing—Ling–2.6–1T integrates seamlessly with mainstream agent frameworks like *Claude Code, OpenClaw, OpenCode, and CodeBuddy*, effortlessly handling multi–tool, multi–step constraints in enterprise environments. ### **Unlocking Robust Intelligence with Superior Efficiency** On [Artificial Analysis](https://artificialanalysis.ai/), **Ling-2.6-1T** achieved an **Intelligence Index of 34** with approximately 16M output tokens, representing a significant generational leap over the previous Ling-1T. This positioning underscores its ability to deliver high-tier intelligence with optimized token consumption.

### **Enhancing Execution Stability for Complex Multi-Step Tasks** Ling-2.6-1T demonstrates balanced excellence across reasoning, coding, and tool-calling, achieving **open-source SOTA** status on multiple execution-heavy benchmarks: * **Advanced Reasoning:** Significantly leads non-thinking models on *AIME26*, showcasing superior complex problem-solving capabilities. * **First-Tier Agent Execution:** Ranks among the top models on *SWE-bench Verified, TAU2-Bench, Claw-Eval, BFCL-V4, and PinchBench*, proving high reliability in real-world workflows. * **Context & Constraints:** Strong performance on *MRCR (16K–256K)* and *IFBench* ensures logical consistency and precision under complex instructions and long contexts.

Note: If you are interested in the previous version, please visit the past model collections on [Huggingface](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI). ## Quickstart ### 🔌 API Usage https://openrouter.ai/inclusionai/ling-2.6-1t:free https://zenmux.ai/inclusionai/ling-2.6-1t ## Deployment ### SGLang #### Environment Preparation ```shell pip install uv uv venv ~/my_ling_env source ~/my_ling_env/bin/activate # uv pip "sglang-kernel>=0.4.1" uv pip install "sglang[all]>=0.5.10.post1" --prerelease=allow ``` #### Run Inference Here is the example to run Ling-1T with 8 GPUs, where the server port is ${PORT}: **Server** **1. Standard Inference (Without MTP)** ```bash sglang serve \ --model-path inclusionAI/Ling-2.6-1T \ --tp-size 8 \ --max-running-requests 32 \ --mem-fraction-static 0.92 \ --chunked-prefill-size 8192 \ --context-length 262144 \ --trust-remote-code \ --model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}' \ --tool-call-parser qwen25 ``` **2. Inference with MTP (Multi-Token Prediction)** _The current official SGLang implementation of MTP contains a bug. For better inference performance, we recommend installing our patched version. Our fix is currently under review and is expected to be merged into the official SGLang library shortly._ **Install our SGLang** ```bash git clone -b ling_2_6 git@github.com:antgroup/sglang.git cd sglang pip install --upgrade pip pip install -e "python" ``` Start server ```bash sglang serve \ --model-path inclusionAI/Ling-2.6-1T \ --tp-size 8 \ --max-running-requests 32 \ --mem-fraction-static 0.92 \ --chunked-prefill-size 8192 \ --context-length 262144 \ --trust-remote-code \ --speculative-algorithm EAGLE \ --speculative-num-steps 3 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --mamba-scheduler-strategy extra_buffer \ --mamba-full-memory-ratio 1.4 \ --model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}' \ --tool-call-parser qwen25 ``` **Client** ```bash curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}' ``` More usage can be found [here](https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-2.6#3-2-ling-2-6-1t) #### vLLM ##### Environment Preparation ```bash pip install uv uv venv ~/my_ling_env source ~/my_ling_env/bin/activate git clone https://github.com/vllm-project/vllm.git cd vllm VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto ``` #### Run inference **Server** ```bash vllm serve $MODEL_PATH \ --port $PORT \ --served-model-name my_model \ --trust-remote-code --tensor-parallel-size 8 \ --gpu-memory-utilization 0.85 ``` **Client** ```bash curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}' ``` ## Limitations & Future Plans While Ling-2.6-1T excels in reasoning and agentic efficiency, our future development will focus on: * **Intelligence-Efficiency Balance:** Further optimizing token efficiency for knowledge-intensive tasks. * **Long-Range Consistency:** Enhancing global consistency in long-term planning and complex information retrieval. * **Dynamic Alignment:** Refining cross-lingual alignment to eliminate occasional language-switching offsets under complex instructions. We remain committed to pushing the boundaries of model performance to enhance delivery efficiency across all complex scenarios. ## License This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ling-V2/blob/main/LICENSE). ## Training content summary The public training-content summary identifying **Ling-2.6-1T** is available below. Please refer to the document for its covered model versions, training-content scope, summary version, and update date. - [Training-content summary (PDF)](https://huggingface.co/inclusionAI/AI-Transparency/resolve/main/Ling-2.6_TDS-Summary.pdf) - [Training-content documentation index](https://huggingface.co/inclusionAI/AI-Transparency) This summary concerns training-content disclosure; it does not replace the model’s technical documentation, usage terms, or license.