Despite its “small” label, this model punches well above its weight, providing excellent performance for the vast majority of use cases where embedding is key.
- Search & Retrieval Engineers: Build fast, scalable semantic search engines with compact, high-quality vectors.
- Recommendation System Developers: Generate embeddings for user behavior, product descriptions, or content metadata.
- NLP Practitioners: Train classification or clustering models on top of semantically rich vector representations.
- Startup Teams & Solo Devs: Reduce costs while still getting great embedding quality for chatbots, apps, or internal tools.
- Data Scientists: Use embeddings for anomaly detection, sentiment analysis, or document similarity tasks.
- Academic Researchers: Apply in large-scale corpus analysis, text clustering, or low-cost NLP experiments.
How to Use text-embedding-3-small?
- Step 1: Choose the Model: Select text-embedding-3-small when calling the v1/embeddings API endpoint.
- Step 2: Format Your Input: Send plain text, sentences, paragraphs, or documents—up to 8,192 tokens in a single call.
- Step 3: Get Your Embeddings: Receive a dense vector (1536 dimensions) that numerically represents the meaning of the input text.
- Step 4: Use Embeddings in Applications: Perform similarity comparison, clustering, vector search, classification, or as input to downstream models.
- Step 5: Store and Index Efficiently: Use vector databases like Pinecone, Weaviate, or FAISS to store and search embeddings.
- Incredibly Affordable: 5x cheaper than text-embedding-3-large, making it perfect for large-scale use.
- Compact Size: With just 1536 dimensions, it strikes a balance between speed, cost, and performance.
- High Accuracy: Nearly matches the performance of larger models on most semantic similarity benchmarks.
- Long Context Handling: Accepts inputs of up to 8192 tokens, ideal for embedding large documents or transcripts.
- Updated Architecture: Part of OpenAI’s 2024 embedding series—faster, smarter, and more optimized.
- Smooth Transition Path: Designed as a drop-in replacement for older models like text-embedding-ada-002.
- Ultra Low-Cost Option: Scalable for millions of embeddings without budget blowout.
- Fast & Lightweight: Lower latency and better throughput for real-time applications.
- High-Quality Vectors: Delivers semantically meaningful vectors with strong benchmark performance.
- Handles Long Documents: Supports longer inputs, reducing the need to chunk text aggressively.
- Easily Integrates with Vector DBs: Works seamlessly with Pinecone, Qdrant, FAISS, and others.
- Slightly Lower Accuracy than Large Model: In edge cases, text-embedding-3-large outperforms.
- No Custom Training: Fine-tuning is not supported; you must use the model as-is.
- No Native Multilingual Support Notes: Performance in non-English text may vary.
- 1536-D Vector Still Relatively Large: Might be overkill for ultra-lightweight applications.
- Model Choice Matters: Users must manually select between small vs. large based on tradeoffs.
1 million tokens
$0.02
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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