Embedding Models Overview

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

1. Input: Raw data (text, image, etc.)
2. Encode: Neural network processes input
3. Output: Fixed-size vector (e.g., 768 dimensions)
4. 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