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

MEXMA

facebook

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 ↗

This is the Investopedia embedding for finance application by the FinLang team. The model is trained using our open-sourced finance dataset from https://huggingface.co/datasets/FinLang/investopedia-embedding-dataset This is a finetuned embedding model on top of BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search in RAG applications. This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.

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 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 ↗

all_datasets_v4_MiniLM-L6

flax-sentence-embeddings

The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained 'MiniLM-L6-H384-uncased' model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developped this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

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

st-codesearch-distilroberta-base

flax-sentence-embeddings

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. It was trained on the code_search_net dataset and can be used to search program code given text. Using this model becomes easy when you have sentence-transformers installed:

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 ↗