1
Jina Embeddings v2
Shares tags: build, models & apis, embeddings
AI Tool
Unlock the Power of Language with OpenAI Embeddings 3
Experience high-accuracy text embeddings for RAG and vector search.
shipped Nov 20, 2025buildpaid
1Achieve optimal performance with our latest models: text-embedding-3-small and text-embedding-3-large.
2Enjoy significantly lower costs, with text-embedding-3-small priced 5x cheaper than previous versions.
3Benefit from enhanced multilingual support, allowing you to reach a broader audience effortlessly.
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2
Jina Embeddings
Shares tags: build, models & apis, embeddings
3
Cohere Embed v3
Shares tags: build, models & apis, embeddings
4
Voyage Embeddings 3
Shares tags: build, models & apis, embeddings
overview
What are OpenAI Embeddings 3?
OpenAI Embeddings 3 delivers advanced text embedding capabilities that facilitate high-accuracy retrieval-augmented generation and semantic search. Whether you're developing AI systems or enhancing content recommendation, these models are designed to meet your needs.
- 1Supports clustering and cross-lingual applications.
- 2Dynamically control embedding dimensions via the API.
- 3Optimized for production AI environments.
features
Key Features
OpenAI Embeddings 3 introduces two innovative models to cater to various requirements. The text-embedding-3-small is ideal for low-latency applications, while text-embedding-3-large ensures the highest accuracy.
- 1Text-embedding-3-small: 5x cheaper with optimized latency.
- 2Text-embedding-3-large: Up to 3072 dimensions for top-tier accuracy.
- 3Improved multilingual performance for better global engagement.
use cases
Use Cases
From AI systems to knowledge retrieval and content recommendation platforms, OpenAI Embeddings 3 can transform your applications. Leverage the versatility of these models to tackle a variety of natural language processing tasks.
- 1Production AI systems for diverse industries.
- 2Semantic search to enhance user experience.
- 3Effortless content recommendations based on user preferences.
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Frequently Asked Questions
+What are the main differences between the small and large models?
The text-embedding-3-small model is optimized for lower latency and cost, making it ideal for real-time applications, while the text-embedding-3-large model provides the highest accuracy for more demanding use cases.
+Can I control the dimensions of the embeddings?
Yes, developers can dynamically adjust the embedding dimensions through the API, allowing you to balance accuracy with storage and compute costs.
+What is the latest knowledge cutoff for these models?
The models do not include knowledge beyond September 2021, but their optimized performance makes them versatile for various NLP tasks.
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For builders
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