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.

Explore AI models 2

Provider Input Output License

Discover models worth your attention

mstsb-paraphrase-multilingual-mpnet-base-v2

AIDA-UPM

This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from sentence-transformers model with Semantic Textual Similarity Benchmark extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences. This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at GitHub. The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below). 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 ↗

CompareSource / docs ↗

star

AIDA-UPM

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 ↗

CompareSource / docs ↗

COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 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.