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

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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

styledistance

StyleDistance

This repository contains the model introduced in StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples. StyleDistance is a **style embedding model** that aims to embed texts with similar writing styles closely and different styles far apart, regardless of content. You may find this model useful for stylistic analysis of text, clustering, authorship identfication and verification tasks, and automatic style transfer evaluation. StyleDistance was contrastively trained on SynthSTEL, a synthetically generated dataset of positive and negative examples of 40 style features being used in text. By utilizing this synthetic dataset, StyleDistance is able to achieve stronger content-independence than other style embeddding models currently available. This particular model was trained using a combination of the synthetic dataset and a real dataset that makes use of authorship datasets from Reddit to train style embeddings. For a version that is purely trained on synthetic data, see this other version of StyleDistance.

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

gte-small

Supabase

_Fork of https://huggingface.co/thenlper/gte-small with ONNX weights to be compatible with Transformers.js. See JavaScript usage._ The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. Performance of GTE models were compared with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.

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

clvp_dev

susnato

**DISCLAIMER** : I do not own any weights present in this repository. All weights belong to the author of the paper - "Better speech synthesis through scaling", James Betker . I am storing the weights(temporarily) for the `tortoise-tts` integration to Huggingface. Please refer to this PR to know more. CLVP model is an integral part of `tortoise-tts` presented in the paper - "Better speech synthesis through scaling" by James Betker. CLVP uses an architecture similar to the CLIP text encoder, except it uses two of them: one for text tokens and the other for MEL tokens.

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

Trained on 50K pairs from the swan07/authorship-verification dataset using: - Learning rate: 2e-5 - Batch size: 16 - Epochs: 4 - Loss: CosineSimilarityLoss swan07/authorship-verification - 325K text pairs from 12 sources including PAN competitions (2011-2020).

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

Boltz2

Synthyra

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。

Basic profile indexed · More details to follow

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 ↗