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探索 AI 模型 6

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We are excited to introduce **LingBot-Video**, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding. > **💡 Rewriter deployment**: the bundled rewriter uses the single-process > `transformers` backend. For higher throughput, deploy the VLM yourself and call > it through an OpenAI-compatible API. Preserve the two-stage semantics: step 1 > must use the base VLM without the rewriter LoRA, while step 2 must use the same > base VLM with the rewriter LoRA enabled. This can be implemented with two > endpoints, or with one server that can select the adapter per request. See > vLLM / SGLang official docs. Install the optional SGLang dependencies only when using SGLang Diffusion or the fused / FP8 MoE runtime:

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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We are excited to introduce **LingBot-World**, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. - **High-Fidelity & Diverse Environments**: It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. - **Long-Term Memory & Consistency**: It enables a minute-level horizon while preserving contextual consistency over time, which is also known as long-term memory. - **Real-Time Interactivity & Open Access**: It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning. - Jan 29, 2026: 🎉 We release the technical report, code, and models for LingBot-World. This codebase is built upon Wan2.2. Please refer to their documentation for installation instructions. Clone the repo:

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视频生成
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We are excited to introduce **LingBot-World**, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. - **High-Fidelity & Diverse Environments**: It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. - **Long-Term Memory & Consistency**: It enables a minute-level horizon while preserving contextual consistency over time, which is also known as long-term memory. - **Real-Time Interactivity & Open Access**: It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning. This codebase is built upon Wan2.2. Please refer to their documentation for installation instructions. Clone the repo: Our model supports video generation at both 480P and 720P resolutions. You can find data samples for inference in the `examples/` directory, which includes the corresponding input images, prompts, and control signals. To enable long video generation, we utilize multi-GPU inference powered by FSDP and DeepSpeed Ulysses. - 480P:

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视频生成
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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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