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价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85001
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查看详情 ↗Minimax-h3-Turbo
lightx2v> 🌐 **Create online with LightX2V Studio** — generate videos with audio from text, images, or reference materials. No local installation or GPU setup required. For local inference and reproducibility, see the MiniMax-H3 Turbo GitHub repository, LightX2V MiniMax-H3 examples, and model specifications. - **Text-to-Audio/Video:** Generate video with audio from text prompts. - **Image-to-Audio/Video:** Generate video with audio from an input image. - **Omni-Reference Audio/Video:** Generate video with audio guided by reference materials.
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查看详情 ↗Qwen-Image-2.1-Lightning
lightx2vA **4-step distilled LoRA for Qwen-Image-2.1**, supporting text-to-image generation and image editing. Training was performed entirely on **16 Huawei Ascend 910B NPUs**, using DMD in LightX2V. For inference on heterogeneous hardware platforms, refer to the LightX2V platform scripts. Both examples use the training noise schedule: four steps with dynamic exponential shifting and no terminal stretching. Training and LightX2V inference keep latents in FP32; these BF16 Diffusers examples can produce different numerical results.
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查看详情 ↗Qwen-Image-2.1-PE-I2I-GGUF-Q8_0
lightx2v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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查看详情 ↗Qwen-Image-2.1-PE-T2I-GGUF-Q8_0
lightx2v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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查看详情 ↗Qwen-Image-2512-Lightning
lightx2vThis model suite supports two mainstream usage frameworks, with detailed guides provided below: 1. Qwen-Image-Lightning Framework For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository 2. LightX2V Framework The models are fully compatible with the LightX2V lightweight video/image generation inference framework. For step-by-step usage examples, configuration templates, and performance optimization tips, see: LightX2V Qwen Image Documentation
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查看详情 ↗Qwen-Image-Edit-2511-Lightning
lightx2vQwen-Image-Edit-2511-Lightning is a collection of optimized models tailored for image editing tasks, leveraging step distillation and quantization techniques to deliver high-efficiency inference performance. This repository hosts three core model files with distinct characteristics: This model suite supports two mainstream usage frameworks, with detailed guides provided below: For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository
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查看详情 ↗Qwen-Image-Edit-Causal
lightx2v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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查看详情 ↗Qwen-Image-Lightning
lightx2vQwen-Image-Lightning provides distilled LoRA weights for Qwen-Image, enabling image generation with fewer inference steps. **Create Online — LightX2V Studio** · **GitHub — Qwen-Image-Lightning** · **GitHub — LightX2V Framework** Studio supports **text-to-image** and **image-to-image** creation directly in your browser, without local installation or GPU setup.
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查看详情 ↗Wan-NVFP4
lightx2v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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查看详情 ↗Wan2.1-Distill-Loras
lightx2v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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查看详情 ↗Wan2.1-Distill-Models
lightx2v*Distillation-accelerated versions of Wan2.1 - Dramatically faster while maintaining exceptional quality* - **4-step inference** (vs traditional 50+ steps) - Up to **2x faster** than ComfyUI - Real-time video generation capability - Multiple resolutions (480P/720P) - Various precision formats (BF16/FP8/INT8) - I2V and T2V support
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查看详情 ↗Wan2.2-Distill-Loras
lightx2v*LoRA weights extracted from Wan2.2 distilled models - Flexible deployment with excellent generation quality* - **Base Model + LoRA**: Can be combined with base models - **Offline Merging**: Pre-merge LoRA into models - **Online Loading**: Dynamically load LoRA during inference - **Multiple Frameworks**: Supports LightX2V and ComfyUI - **High Noise**: More creative, diverse outputs - **Low Noise**: More faithful to input, stable outputs - Rank 64 LoRA, compact size
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查看详情 ↗Wan2.2-Distill-Models
lightx2v*Distillation-accelerated version of Wan2.2 - Dramatically faster speed with excellent quality* > 🌐 **Create online with LightX2V Studio** — > an AI avatar and multimodal content production platform built on LightX2V. > Use Wan2.2 video generation in your browser, with no local setup required. > See the online usage guide below. --- - 2026.04.12: We are excited to release the Wan2.2-I2V-A14B-4step-720p-high and Wan2.2-I2V-A14B-4step-720p-low models. Compared to previous iterations, this version was trained on a high-quality 720p dataset and features an optimized low-noise training algorithm. These enhancements significantly boost the model's performance in fine-grained detail rendering and visual texture.
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查看详情 ↗Z-Image-Turbo-Quantized
lightx2v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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查看详情 ↗depth-anything-base-hf
LiheYoungDepth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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查看详情 ↗depth-anything-large-hf
LiheYoungDepth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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查看详情 ↗depth-anything-small-hf
LiheYoungDepth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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查看详情 ↗depth_anything_vitb14
LiheYoung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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查看详情 ↗depth_anything_vitl14
LiheYoung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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查看详情 ↗depth_anything_vits14
LiheYoung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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查看详情 ↗a-fixed-output-model
lijie1228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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查看详情 ↗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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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
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。