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 1529

Discover models worth your attention

cvt-13

Microsoft

CvT-13 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team. Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

cvt-13-384

Microsoft

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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

cvt-21

Microsoft

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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

cvt-21-384

Microsoft

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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in this repository. Note that DiT is identical to the architecture of BEiT. Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team. The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in this repository. Note that DiT is identical to the architecture of BEiT. Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team. The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

focalnet-tiny

Microsoft

FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Focal Modulation Networks by Yang et al. and first released in this repository. Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team. Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

resnet-26

Microsoft

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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

resnet-50

Microsoft

ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.

Image classification
Input Image→ Output Not verified
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 ↗