text-embedding-3-large

text-embedding-3-large is OpenAI's flagship embedding model released in January 2024, supporting up to 3072 dimensions and representing OpenAI's "new best performing model" for embeddings.

Performance Improvements

Compared to text-embedding-ada-002, text-embedding-3-large delivers significant improvements:

• MIRACL Benchmark: Average score jumped from 31.4% to 54.9% (74% improvement)
• MTEB Benchmark: Average score increased from 61.0% to 64.6%

This makes it one of the top-performing commercial embedding models in 2024-2025.

Core Features

Matryoshka Representation Learning

Using Matryoshka representation learning, developers can specify output dimensions from 256 to 3072. Using 1024 dimensions saves 67% storage while maintaining 95%+ retrieval quality.

Multilingual Support

While primarily optimized for English, text-embedding-3-large demonstrates strong performance across 100+ languages, suitable for multilingual search and cross-lingual retrieval.

Ecosystem Integration

Native OpenAI model with seamless integration to ChatGPT, GPT-4, and the entire OpenAI API ecosystem.

Use Cases

• RAG Systems: Powering retrieval for GPT-4 and other LLMs
• Semantic Search: Building intelligent search engines that understand user intent
• Recommendation Engines: Finding similar content based on semantic similarity
• Document Clustering: Organizing large document collections by topic
• Q&A Systems: Matching questions to relevant answers in knowledge bases

Pricing

• Standard: $0.13 per 1M tokens
• Promotional: Some reports indicate $0.065 per 1M tokens (verify current rates)

Cost Comparison

• text-embedding-3-small: $0.02 per 1M tokens (87% cheaper, 95% performance)
• Cohere Embed v3: $0.10 per 1M tokens
• Open-Source (BGE-M3, E5): Free to self-host with infrastructure costs

Pros & Cons

Pros:

• State-of-the-art retrieval performance (54.9% MIRACL)
• Matryoshka flexibility saves 67% storage costs
• Native OpenAI ecosystem integration
• Supports 100+ languages

Cons:

• Higher cost at scale ($0.13 per 1M tokens)
• Multilingual performance lags specialized models
• Cloud-only deployment with vendor lock-in
• Cannot fine-tune for domain-specific needs

For teams building RAG and semantic search on OpenAI infrastructure, text-embedding-3-large is the natural choice. For cost-sensitive or multilingual-heavy workloads, evaluate open-source alternatives like BGE-M3.