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
84,995 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 fine-tuned version of sentence-transformers/all-mpnet-base-v2 for schema matching tasks in the biomedical domain, specifically targeting the GDC (Genomic Data Commons) data standard. The model was fine-tuned using a self-supervised approach that leverages LLMs to generate diverse training data from the GDC target schema. The key innovation is combining LLM-based augmentation with structure-based augmentation: **1. LLM-Based Augmentation (`llm-aug`):** - For each anchor column in the GDC target schema, an LLM generates semantically equivalent but syntactically diverse variations - Captures syntactic heterogeneity common in real biomedical datasets - Example transformations: - `patient_id` → `subject_identifier`, `participant_number`, `patient_identifier` - `diagnosis_date` → `date_of_diagnosis`, `dx_date`, `diagnostic_date` - Creates diverse positive examples while preserving semantic meaning

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

Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. @article{bartpho, title = {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}}, author = {Nguyen Luong Tran and Duong Minh Le and Dat Quoc Nguyen}, journal = {arXiv preprint}, volume = {arXiv:2109.09701}, year = {2021} } **Please CITE** our paper when BARTpho is used to help produce published results or incorporated into other software.

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

A **multilingual embedding model** with 32K context window and **2D Matryoshka** support for flexible efficiency-quality tradeoffs. 1. **Dimension Reduction** (Matryoshka): Truncate embeddings to smaller dimensions with minimal quality loss 2. **Layer Reduction** (Adaptive): Use intermediate layer outputs for faster inference mmBERT-Embed is significantly faster due to: 1. **Flash Attention 2** - BGE-M3 lacks FA2 (O(n) vs O(n²) attention) 2. **Encoder architecture** - Qwen3 uses decoder with causal masking 3. **Smaller model** - 307M vs 569M/600M params

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

Connect requests with the right context. Vela Embedding powers multilingual semantic matching, retrieval, and memory in your router. Compared with the original mmBERT Embedding. Scores are ×100; higher is better. Both models use 22 layers, 768 dimensions and FP32. The MTEB rows use each task's standard main metric; SummScreenFD uses its validation split.

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 ↗