AI NEWS.ccAI is happening. The future is updating.
♡ Saved tools 0
AI NEWS · Model intelligenceExplore capabilities. Make informed choices. ↗Profiles indexed first · Details continuously enriched
THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Popular searches
84,991 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
DISCOVER & COMPARE

Explore AI models 2043

Discover models worth your attention

🤗 MERT-v2-FullSong Music representations with full-song context 24 kHz mono · 632M parameters · 24 layers · 1,024 dimensions · 25 Hz 🎵 YuE2 project · 🚀 Quick start · 📊 MARBLE · 🎛️ Layer guide · 📄 arXiv · PDF · 📚 Citation **MERT-v2-FullSong is a bidirectional music encoder adapted to complete songs lasting 30–360 seconds.** Extract general-purpose music representations at the frame or recording level. Load with standard Hugging Face Transformers, using the familiar MERT workflow.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

🤗 SheetSage2 Music audio to editable scores Melody · Chords · Beats · Key · Structure 🎵 YuE2 project · 💻 Code · 🚀 Quick start · 📊 Benchmarks · 📄 SheetSage2 arXiv · 📄 YuE2 arXiv · YuE2 PDF · 📚 Citation **SheetSage2 turns music recordings into lead sheets and timed musical annotations.** Transcribe a complete song, edit its ABC or MIDI, render a piano preview, or extract embeddings and token predictions for your own tools.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

🎧 Demo · 🚀 Quick start · ⚡ Speed · 📊 Benchmarks · 📄 arXiv · PDF · 📚 Citation **YuE2-Vae turns acoustic latents into 48 kHz stereo music.** It is the default decoder for song generation and listening, with full-song decoding, an audio encoder, and Hugging Face loading. Use it with **🤗 YuE2-3B** for full-song generation, or load it on its own to encode and decode audio. 🤗 Alternative decoder: YuE2-Vae-legacy.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

- 中英双语，以及中英跨语种能力(Bilingual and Crosslingual capability in English and Chinese)； - RAG优化，适配更多真实业务场景(RAG adaptation for more domains, including Education, Law, Finance, Medical, Literature, FAQ, Textbook, Wikipedia, etc.)； - 方便集成进langchain和llamaindex(Easy integrations for langchain and llamaindex in BCEmbedding )。 - `EmbeddingModel`不需要“精心设计”instruction，尽可能召回有用片段。 (No need for "instruction") - **最佳实践（Best practice）** ：embedding召回top50-100片段，reranker对这50-100片段精排，最后取top5-10片段。（1. Get top 50-100 passages with bce-embedding-base_v1 for "`recall`"； 2. Rerank passages with bce-reranker-base_v1 and get top 5-10 for "`precision`" finally. ） - `BCEmbedding`技术博客（ **Technical Blog** ）: 为RAG而生-BCEmbedding技术报告 - Related link for **RerankerModel** : bce-reranker-base_v1 - RAG applications: QAnything, ragflow, HuixiangDou, ChatPDF. - Efficient inference framework: ChatLLM.cpp, Xinference, mindnlp (Huawei GPU, 华为GPU).

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

scincl

malteos

SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers. It uses the citation graph neighborhood to generate samples for contrastive learning. Prior to the contrastive training, the model is initialized with weights from scibert-scivocab-uncased. The underlying citation embeddings are trained on the S2ORC citation graph. Paper: Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (EMNLP 2022 paper). These model weights are the ones that yielded the best results on SciDocs (`seed=4`). In the paper we report the SciDocs results as mean over ten seeds.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ
How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

AI NEWS · From information to decisions

Follow trends. Find tools. Explore models.

Explore AI news ↗Discover AI tools ↗