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

84,737 款模型档案13040 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 3

厂商输入输出许可

为你发现值得关注的模型

fuyu-8b

adept

We’re releasing Fuyu-8B, a small version of the multimodal model that powers our product. The model is available on HuggingFace. We think Fuyu-8B is exciting because: 1. It has a much simpler architecture and training procedure than other multi-modal models, which makes it easier to understand, scale, and deploy. 2. It’s designed from the ground up for digital agents, so it can support arbitrary image resolutions, answer questions about graphs and diagrams, answer UI-based questions, and do fine-grained localization on screen images. 3. It’s fast - we can get responses for large images in less than 100 milliseconds. 4. Despite being optimized for our use-case, it performs well at standard image understanding benchmarks such as visual question-answering and natural-image-captioning. Please note that **the model we have released is a base model. We expect you to need to finetune the model for specific use cases like verbose captioning or multimodal chat.** In our experience, the model responds well to few-shotting and fine-tuning for a variety of use-cases. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. Image patches are instead linearly projected into the first layer of the transformer, bypassing the embedding lookup. We simply treat the transformer decoder like an image transformer (albeit with no pooling and causal attention). See the below diagram for more details.

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视觉问答

输入文本 · 图像→ 输出文本

价格待核验多模态与渠道费用须分别核对

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加入对比来源 / 文档 ↗

persimmon-8b-base

adept

At Adept, we’re working towards an AI agent that can help people do anything they need to do on a computer. We’re not in the business of shipping isolated language models (LMs)—this was an early output of the model scaling program that will support our products. We trained it from scratch using a context size of 16K. Many LM use cases are context-bound; our model has 4 times the context size of LLaMA2 and 8 times that of GPT-3, MPT, etc.

译文排队中 · 当前显示来源原文

文本生成

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

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加入对比来源 / 文档 ↗

persimmon-8b-chat

adept

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

文本生成

输入文本→ 输出文本

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。

找到适合你的 AI 模型。

探索 AI 模型 3

fuyu-8b

persimmon-8b-base

persimmon-8b-chat

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。