AI NEWS.ccAI正在发生 未来正在更新
♡ 我的收藏 0
AI NEWS · 模型情报站发现能力边界，让每一次选择更有依据 ↗基础档案先收录 · 缺失资料持续补齐
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
84,085 款模型档案12951 家厂商 / 发布者45 个能力分类464 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 5

为你发现值得关注的模型

drama-3

fishaudio

**Fish Audio Drama 3** is Fish Audio's new-generation text-to-speech model, built for multi-character scenes and long-form content. It supports multi-speaker generation, voice cloning and natural-language control. - **35B parameters, 3B active per token**: MoE text backbone with hybrid (full + linear) attention - **Codebook-MTP audio decoder**: 10 codebooks decoded causally over the whole sequence - **FP8 MoE experts** with the remaining weights in bf16 - **Speaker tags** ` ` … ` ` — one text can carry up to 16 characters - **Long text** is chunked automatically and can be streamed per chunk Drama 3 runs on SGLang-Omni from launch day. For installation, deployment, and streaming API examples, see the Drama 3 Cookbook.

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 ↗
来源 / 文档 ↗

drama-3-int4

fishaudio

**The smallest working Drama 3.** Unofficial int4 quantization of fishaudio/drama-3 — Fish Audio's 35B (3B active) multi-speaker TTS model — with **real eval numbers** and one-command serving through SGLang Omni. The two checkpoints were compared on one H200 with the default SGLang Omni server settings: the 1,088 Seed-TTS English prompts, each cloning the voice of its own reference clip, sent as non-streaming requests at concurrency 8. WER is computed on the generated audio transcribed by Qwen3-ASR-1.7B. The int4 checkpoint is about 8% slower per request and its WER stays at the FP8 level: the gap between the two is about as large as the run-to-run spread of one checkpoint (a second FP8 run measured 1.365 s mean latency and 2.02% WER).

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 ↗
来源 / 文档 ↗

**Fish Speech V1.5** is a leading text-to-speech (TTS) model trained on more than 1 million hours of audio data in multiple languages. Supported languages: - English (en) >300k hours - Chinese (zh) >300k hours - Japanese (ja) >100k hours - German (de) ~20k hours - French (fr) ~20k hours - Spanish (es) ~20k hours - Korean (ko) ~20k hours - Arabic (ar) ~20k hours - Russian (ru) ~20k hours - Dutch (nl) <10k hours - Italian (it) <10k hours - Polish (pl) <10k hours - Portuguese (pt) <10k hours

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 ↗
来源 / 文档 ↗

s2-pro

fishaudio

**Fish Audio S2 Pro** is a leading text-to-speech (TTS) model with fine-grained inline control of prosody and emotion. Trained on over 10M+ hours of audio data across 80+ languages, the system combines reinforcement learning alignment with a dual-autoregressive architecture. The release includes model weights, fine-tuning code, and an SGLang-based streaming inference engine. S2 Pro builds on a decoder-only transformer combined with an RVQ-based audio codec (10 codebooks, ~21 Hz frame rate) using a **Dual-Autoregressive (Dual-AR)** architecture: - **Slow AR** (4B parameters): Operates along the time axis and predicts the primary semantic codebook. - **Fast AR** (400M parameters): Generates the remaining 9 residual codebooks at each time step, reconstructing fine-grained acoustic detail.

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 ↗
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。

1 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。

AI NEWS · 从信息到选择

资讯看趋势，工具提效率，模型筑可能。

探索 AI 资讯 ↗发现 AI 工具 ↗