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

84,991 款模型档案13202 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 6

厂商输入输出许可

为你发现值得关注的模型

tabpfn_2_5

Prior-Labs

TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN. For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format.

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表格与时序预测

输入待核验→ 输出待核验

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tabpfn_2_6

Prior-Labs

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

表格与时序预测

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

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tabpfn_3

Prior-Labs

TabPFN-3 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/TabPFN. More details can be found in the Model Report. For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! TabPFN-3 ships with default classification and regression checkpoints, plus a few experimental specialized variants. We recommend starting with the defaults — the variants can be useful in ensembling or HPO setups, or tried manually in the regime they were trained for. Their name suffixes indicate what we expect them to be good at.

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表格与时序预测

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价格待核验多模态与渠道费用须分别核对

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tabpfn_3_5

Prior-Labs

TabPFN-3.5 is a transformer-based foundation model that uses in-context learning to solve tabular prediction problems in a forward pass. One checkpoint serves both classification and regression. Inference code can be found at https://github.com/PriorLabs/TabPFN. `TabPFNRegressor` works the same way, off the same checkpoint. For more examples, see the github repo: https://github.com/PriorLabs/TabPFN. Every checkpoint ships with basic text and datetime column handling enabled (`TRANSFORM_TEXT`, `TRANSFORM_DATES`) and a feature limit of 20,000.

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表格与时序预测

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TabPFN-v2-clf

Prior-Labs

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular datasets without requiring task-specific training. - **Developed by:** Prior Labs - **Model type:** Transformer-based foundation model for tabular data - **License:** Prior Labs License (Apache 2.0 with additional attribution requirement) - **Paper:** Published in Nature (January 2025) - **Repository:** GitHub - priorlabs/tabpfn 📚 For detailed usage examples and best practices, check out: - Interactive Colab Tutorial

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表格与时序预测

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价格待核验多模态与渠道费用须分别核对

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TabPFN-v2-reg

Prior-Labs

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training. - **Developed by:** Prior Labs - **Model type:** Transformer-based foundation model for tabular data - **License:** Prior Labs License (Apache 2.0 with additional attribution requirement) - **Paper:** Published in Nature (January 2025) - **Repository:** GitHub - priorlabs/tabpfn 📚 For detailed usage examples and best practices, check out: - Interactive Colab Tutorial

译文排队中 · 当前显示来源原文

表格与时序预测

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。

找到适合你的 AI 模型。

探索 AI 模型 6

tabpfn_2_5

tabpfn_2_6

tabpfn_3

tabpfn_3_5

TabPFN-v2-clf

TabPFN-v2-reg

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。