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

84,963 款模型档案13181 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 3

厂商输入输出许可

为你发现值得关注的模型

MOSS-TTS-v1.5-GGUF

christopherthompson81

Self-contained GGUF packages of OpenMOSS-Team/MOSS-TTS-v1.5, for audio.cpp. Each file carries the 8B delay-pattern backbone, the MOSS-Audio-Tokenizer v1 codec (**both** encoder and decoder, because cloning needs the encoder), the tokenizer and the model spec — nothing else to download. q4_k is usable and clones as well as the others — but it is **not lossless on the words**. Across four takes of the same sentence at different seeds, one substituted an article ("the" for "this"); q8_0 was exact on all four. Two independent ASR models agree on that substitution, so it is the audio and not a transcription artifact. Four takes of one sentence is a small sample and the true rate is not pinned down by it. Treat q4_k as the option for when 9.3 GB versus 13.2 GB decides whether the model runs at all, and prefer q8_0 when it fits.

译文排队中 · 当前显示来源原文

语音合成

输入文本→ 输出音频

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 ↗

MOSS-TTSD-GGUF

christopherthompson81

Self-contained GGUF packages of OpenMOSS-Team/MOSS-TTSD-v1.0, for audio.cpp. Each file carries the 8B delay-pattern backbone, the MOSS-Audio-Tokenizer v1 codec (**both** encoder and decoder, because cloning needs the encoder), the tokenizer and the model spec — nothing else to download. MOSS-TTSD speaks a speaker-tagged dialogue in one take: `[S1]` and `[S2]` mark who is talking, and each tag can carry its own reference recording. `OpenMOSS-Team/MOSS-TTSD-v1.0` ships no codec weights — its processor resolves `OpenMOSS-Team/MOSS-Audio-Tokenizer` at load time — so both are listed as parent models. This checkpoint reads the first 16 of that codec's 32 RVQ layers.

译文排队中 · 当前显示来源原文

语音合成

输入文本→ 输出音频

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 ↗

VibeVoice-ASR-Streaming-1.5B-GGUF

christopherthompson81

This repository contains audio.cpp-native GGUF builds of `microsoft/VibeVoice-ASR-Streaming-1.5B`. The 1.5B checkpoint runs through the same audio.cpp loader as the 7B with no code changes: the layer count, hidden size, and head counts are read from the checkpoint's own `config.json`, and the tensor names are identical. Each GGUF is self-contained and embeds the audio.cpp package spec and required sidecars.

译文排队中 · 当前显示来源原文

语音识别

输入音频→ 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

MOSS-TTS-v1.5-GGUF

MOSS-TTSD-GGUF

VibeVoice-ASR-Streaming-1.5B-GGUF

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。