text-embedding-3-large (OpenAI)
Basic Information
- Company/Brand: OpenAI
- Country/Region: USA
- Official Website: https://platform.openai.com/docs/models/text-embedding-3-large
- Type: Text Embedding Model
- Release Date: January 2024
- Model Series: OpenAI Embeddings
Product Description
text-embedding-3-large is OpenAI's most powerful text embedding model, capable of converting text into vector representations of up to 3072 dimensions. It is suitable for both English and non-English tasks and is widely used in scenarios such as semantic search, text clustering, recommendation systems, anomaly detection, and classification. As the flagship product of OpenAI's embedding model family, it excels in precision and multilingual capabilities.
Core Features/Characteristics
- High-Dimensional Embedding: Up to 3072-dimensional vector representation, providing rich semantic information
- Matryoshka Representation Learning: Supports vector dimension pruning, allowing trade-offs between precision and efficiency as needed
- Multilingual Support: Performs well on both English and non-English tasks
- Batch Processing: Supports Batch API with a 50% discount
- Simple API: Easy to call via OpenAI API, integration can be done with just a few lines of code
- Broad Compatibility: Deep integration with mainstream vector databases and RAG frameworks
Technical Specifications
- Maximum Dimensions: 3072
- Maximum Input Tokens: 8191
- Model Architecture: Transformer-based embedding model
- Output Format: Floating-point vector
Business Model
- Standard Price: $0.13 / million input tokens
- Batch Price: $0.065 / million input tokens (Batch API, 50% discount)
- Input-Only Billing: No output token fees
- Pay-as-You-Go: No monthly fees, pay only for what you use
Comparison with Same Series Models
| Feature | text-embedding-3-large | text-embedding-3-small |
|---|---|---|
| Dimensions | 3072 | 1536 |
| Price | $0.13/MTok | $0.02/MTok |
| Precision | Higher | High |
| Use Case | Precision-first | Cost-first |
Target Users
- RAG system developers
- Semantic search application developers
- AI application teams requiring high-precision embeddings
- Developers already using OpenAI API
- Recommendation and classification system developers
Competitive Advantages
- OpenAI brand endorsement, reliable and stable
- Simple API usage with comprehensive documentation
- Matryoshka learning supports flexible dimension-precision trade-offs
- Batch API offers a 50% discount, more economical for large-scale use
- Seamless integration with other OpenAI products (e.g., GPT-4)
Limitations
- Closed-source model, cannot be deployed locally
- Requires network calls, with potential latency and availability risks
- Data needs to be sent to OpenAI servers, privacy-sensitive scenarios require caution
- MTEB score (64.6) slightly lower than Cohere embed-v4 (65.2)
- Price ($0.13/MTok) is relatively high among commercial models
Relationship with OpenClaw Ecosystem
text-embedding-3-large can serve as one of the default embedding models for OpenClaw RAG systems. For users already utilizing the OpenAI API, the same API key can be used to access both GPT models and embedding models, simplifying the integration process. The high version is suitable for scenarios requiring high retrieval precision, while the small version is ideal for cost-sensitive individual users.