520 AI Models
Model coverage, transparent pricing, and API-ready metadata in one gateway catalog.
| Provider | Model | Input | Output | Context |
|---|---|---|---|---|
Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications. EmbeddingMar 12, 20263 capabilities | Input$0.6/1M tokens | Output$2.40/1M tokens | Context8K | |
Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines. EmbeddingFeb 9, 20262 capabilities | Input$0.075/1M tokens | Output$0.3/1M tokens | Context128K | |
Mistral Embed is Mistral AI's text embedding model, built for semantic search and RAG workflows. It generates 1024-dimensional vectors that capture meaningful relationships between pieces of text. EmbeddingOct 30, 20251 capability | Input$0.125/1M tokens | Output$0/1M tokens | Context8K | |
Mistral Codestral Embed is an embedding model specialized for code, ideal for indexing repositories and powering coding assistants with high-quality code retrieval. EmbeddingOct 29, 20251 capability | Input$0.1875/1M tokens | Output$0/1M tokens | Context8K | |
text-embedding-3-small is an upgraded, efficient replacement for the Ada embedding model. It generates numeric text representations for similarity tasks and is useful for search, clustering, recommendations, anomaly detection, and classification. EmbeddingJan 24, 20241 capability | Input$0.014/1M tokens | Output$0.014/1M tokens | Context8K | |
text-embedding-3-large is OpenAI's most powerful embedding model, producing numeric representations of text to measure similarity. It works well for both English and non-English content and is widely used for tasks like search, clustering, recommendations, anomaly detection, and classification. EmbeddingJan 24, 20241 capability | Input$0.091/1M tokens | Output$0.091/1M tokens | Context8K | |
text-embedding-ada-002 is OpenAI's older, legacy text embedding model. EmbeddingDec 21, 20221 capability | Input$0.07/1M tokens | Output$0.07/1M tokens | Context8K | |
Jina Embeddings V5 Text Small by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina Embeddings V5 Text Nano by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina Embeddings V2 Base ES by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina Embeddings V2 Base DE by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina Embeddings V4 by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina Embeddings V3 by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina ColBERT V1 EN by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina Colbert V2 by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K | |
Jina CLIP V1 by Jina AI. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests. Embedding | Input$0.05/1M tokens | Output$0/1M tokens | Context8K |