THE AI MODEL DIRECTORY 模型探索，从这里开始

找到适合你的 AI 模型。

从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。

热门探索

84,836 款模型档案13097 家厂商 / 发布者45 个能力分类465 款有可比报价 · 30 款有公开评测

T文本生成 ▧图像生成 ▷视频生成 ⌘代码生成 ◖语音合成 ♫音乐生成 ⬡3D 建模 ▤文档分析

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探索 AI 模型 2

厂商输入输出许可

为你发现值得关注的模型

Qwen-Image-2.1-viggle-turbo

Viggle

**Built with Qwen.** A few-step distilled version of Qwen/Qwen-Image-2.1 by Viggle. Text-to-image and instruction-driven editing with 1–3 reference images in **6 steps instead of 40**, with **no classifier-free guidance**. About **5× faster** than the 40-step base model end to end, and very competitive with it in quality: on the official Qwen examples the two are hard to tell apart on most prompts. The clearest gaps are small, dense text and complicated edits (Known limitations). Compare them yourself in the **Comparison** tab of the demo Space. * **6 steps, a different balance.** Against v0.2.1: less grain and cleaner surfaces, fine texture a little softer; diversity (still close to the base model's) and small-text accuracy about the same. It is not a strict upgrade: if you prefer the crisper look, v0.2.1 is still in the repository. * **New 9-step mode:** 7 turbo steps, then the LoRA is switched off and the base model finishes the last two. Finer detail, and small text comes out right more often (not always). It takes about 1.4–1.5× as long as 6 steps (still about 3.5× faster than the base model). It works in diffusers and the demo Space only (9 steps). * **We think 6 steps is close to its capacity.** Since v0.2.1, every gain we found at 6 steps cost something elsewhere: sharper came with more grain, less grain came with a softer look. Beyond this point, quality most likely has to be paid for with steps, which is what the 9-step mode does.

译文排队中 · 当前显示来源原文

图像生成

输入文本→ 输出图像

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

Viggle-Animate

Viggle

**Try the demo**  ·  **Research write-up**  ·  **ComfyUI nodes**  ·  **viggle.ai/h3**  ·  Built on **MiniMaxAI/MiniMax-H3** Made with Viggle-Animate by @cocktailpeanut , not by us. Click through to watch it on X. White House Funk: a preview of Viggle-Animate v2, coming soon. Not made with the weights in this repository. The cast of a two-minute music video replaced through its fast cuts, from at most one repainted frame per shot. AI-generated parody: the people shown did not take part in, approve or endorse it.

译文排队中 · 当前显示来源原文

分类待补充

输入待核验→ 输出待核验

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

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。

每月输入 Token 10 M每月输出 Token 2 M1 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。