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

DISCOVER & COMPARE

探索 AI 模型 4

厂商输入输出许可

为你发现值得关注的模型

pico-decoder-large

pico-lm

**pico-decoder-large** is the largest model (570M) in the current `pico-decoder` suite. It is a full-scale research model designed for in-depth interpretability studies of transformer learning. Trained with `pico-train` and fully compatible with `pico-analyze`, it offers rich checkpointing and analytical insight into large-scale LM behavior. This model supports fine-grained analysis using pico-analyze. This tool enables researchers to understand how learning unfolds over training, even at very small scales. ```bibtex @software{pico2025, author = {Diehl Martinez, Richard}, title = {Pico: A Lightweight Framework for Studying Language Model Learning Dynamics}, year = {2025}, url = {https://github.com/pico-lm} }

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

查看详情 ↗

加入对比来源 / 文档 ↗

pico-decoder-medium

pico-lm

**pico-decoder-medium** is a 181M parameter model in the `pico-decoder` suite, balancing scale and analyzability. Built with `pico-train` and instrumented with `pico-analyze`, it enables detailed studies of layer-wise learning behavior during language model pretraining. This model supports fine-grained analysis using pico-analyze. This tool enables researchers to understand how learning unfolds over training, even at very small scales. ```bibtex @software{pico2025, author = {Diehl Martinez, Richard}, title = {Pico: A Lightweight Framework for Studying Language Model Learning Dynamics}, year = {2025}, url = {https://github.com/pico-lm} }

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

查看详情 ↗

加入对比来源 / 文档 ↗

pico-decoder-small

pico-lm

**pico-decoder-small** is a 65M parameter model in the `pico-decoder` suite — a lightweight, LLaMA-style decoder-only transformer trained from scratch using `pico-train`. It is designed for transparent and reproducible research into the learning dynamics of language models, and is fully compatible with the `pico-analyze` toolkit for detailed interpretability analysis. This model supports fine-grained analysis using `pico-analyze`. This tool enables researchers to understand how learning unfolds over training, even at modest scales. ```bibtex @software{pico2025, author = {Diehl Martinez, Richard}, title = {Pico: A Lightweight Framework for Studying Language Model Learning Dynamics}, year = {2025}, url = {https://github.com/pico-lm} }

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

查看详情 ↗

加入对比来源 / 文档 ↗

pico-decoder-tiny

pico-lm

**pico-decoder-tiny** is the smallest (11M) model in the `pico-decoder` suite — a lightweight, LLaMA-style decoder-only transformer trained from scratch using `pico-train`. It is designed for transparent and reproducible research into the learning dynamics of language models, and is fully compatible with the `pico-analyze` toolkit for detailed interpretability analysis. This model supports fine-grained analysis using `pico-analyze`. This tool enables researchers to understand how learning unfolds over training, even at very small scales.

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

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 4

pico-decoder-large

pico-decoder-medium

pico-decoder-small

pico-decoder-tiny

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。