Embedding Models Overview
Neural networks that convert text, images, or other data into dense vector representations. Enable semantic understanding by mapping similar concepts to nearby points in vector space.
About this tool
Overview
Embedding models transform raw data (text, images, audio) into dense numerical vectors that capture semantic meaning, enabling AI applications to understand and compare content.
How They Work
- Input: Raw data (text, image, etc.)
- Encode: Neural network processes input
- Output: Fixed-size vector (e.g., 768 dimensions)
- Property: Similar inputs → similar vectors
Types
Text Embeddings
- Sentence-BERT
- BGE models
- OpenAI text-embedding-*
- Cohere Embed
Multimodal
- CLIP (text + image)
- Gemini Embedding 2
- UForm
Specialized
- Code embeddings
- Audio embeddings
- Graph embeddings
Key Characteristics
Dimensionality
- Small: 384-512 (fast, less info)
- Medium: 768-1024 (balanced)
- Large: 1536-4096 (rich, slower)
Context Window
- Max input length
- Typically 512-8192 tokens
- Affects chunk size decisions
Choosing Models
Considerations
- Task: Search, classification, clustering
- Language: Monolingual vs multilingual
- Domain: General vs specialized
- Performance: Speed vs quality
- Cost: Self-hosted vs API
Evaluation
- MTEB Leaderboard
- Task-specific benchmarks
- Domain testing
Popular Models (2026)
- NV-Embed (NVIDIA)
- BGE-M3 (BAAI)
- Gemini Embedding 2
- text-embedding-3-* (OpenAI)
- Voyage embeddings
- Jina Embeddings v4/v5
Pricing
- Open Source: Free (hosting costs)
- APIs: Usage-based pricing
- Self-hosted: Compute infrastructure
Surveys
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Information
Websitewww.bentoml.com
PublishedMar 11, 2026
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