OpenAI text-embedding-3-small vs large comparison
text-embedding-3-small model offers faster and cheaper embeddings with slightly lower accuracy, while text-embedding-3-large provides higher-quality embeddings at a higher cost and latency. Choose text-embedding-3-large for tasks needing precise semantic understanding and text-embedding-3-small for cost-sensitive or high-throughput scenarios.VERDICT
text-embedding-3-large for accuracy-critical semantic search and analysis; use text-embedding-3-small for faster, cost-efficient embedding generation at scale.| Model | Embedding Dimension | Speed | Cost per 1K embeddings | Best for | Free tier availability |
|---|---|---|---|---|---|
| text-embedding-3-small | 384 | Faster | Lower | High-volume, cost-sensitive tasks | Yes |
| text-embedding-3-large | 1536 | Slower | Higher | High-accuracy semantic search and analysis | Yes |
Key differences
text-embedding-3-small outputs 384-dimensional vectors, making it faster and cheaper but with less semantic nuance. text-embedding-3-large produces 1536-dimensional embeddings, capturing richer semantic information at the cost of higher latency and price. The smaller model suits bulk embedding tasks, while the larger excels in precision-demanding applications.
Side-by-side example
Generate embeddings for the same text using both models with the OpenAI Python SDK v1+.
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
text = "OpenAI provides powerful embedding models for semantic search."
# Small embedding
response_small = client.embeddings.create(
model="text-embedding-3-small",
input=text
)
embedding_small = response_small.data[0].embedding
print(f"Small embedding length: {len(embedding_small)}")
# Large embedding
response_large = client.embeddings.create(
model="text-embedding-3-large",
input=text
)
embedding_large = response_large.data[0].embedding
print(f"Large embedding length: {len(embedding_large)}")Small embedding length: 384 Large embedding length: 1536
When to use each
Choose text-embedding-3-large when embedding quality directly impacts downstream tasks like semantic search, recommendation, or clustering. Opt for text-embedding-3-small when embedding large datasets quickly and cost-effectively, such as for approximate nearest neighbor indexing or bulk classification.
| Use case | Recommended model |
|---|---|
| Semantic search with high precision | text-embedding-3-large |
| Bulk embedding for large datasets | text-embedding-3-small |
| Real-time embedding generation with latency constraints | text-embedding-3-small |
| Fine-grained clustering or recommendation | text-embedding-3-large |
Pricing and access
Both models are accessible via the OpenAI API with free tier usage subject to OpenAI's current policy.
| Option | Free | Paid | API access |
|---|---|---|---|
| text-embedding-3-small | Yes | Yes, lower cost | OpenAI API |
| text-embedding-3-large | Yes | Yes, higher cost | OpenAI API |
Key Takeaways
-
text-embedding-3-largeoffers richer semantic embeddings at higher cost and latency. -
text-embedding-3-smallis optimized for speed and cost-efficiency with smaller embedding vectors. - Select the model based on your application's accuracy needs versus throughput and budget constraints.