text-embedding-3-small API in India

In Indian rupees, the currency your card is charged in, with the published US dollar rate beside each.

Every rate is published and stays the same from one request to the next. See every model.

Prices are our published US dollar rates converted at ₹98.00 to the dollar, our rate for October — the same rate your card is charged.

text-embedding-3-small is the inexpensive half of OpenAI’s embedding pair. It produces 1,536 numbers per text — the same size as the older ada-002, so it drops into an existing search index — and beats ada-002 on both the MTEB benchmark (62.3% against 61.0%) and multilingual search.

For English-language search and recommendations it is close enough to the large model that the saving usually wins. Where documents are in several languages, the large model’s lead is wide enough to be worth paying for.

• Semantic search and retrieval on a budget.
• Finding duplicates and grouping similar items.
• Recommendations.

• Noticeably weaker than text-embedding-3-large on multilingual search.
• Each piece of text can be at most 8,192 tokens.

Roar AI provides access to text-embedding-3-small through the Roar AI gateway, charged in Indian rupees on one bill with every other model. Requests from India go through our gateway in Bangalore. Developers can use text-embedding-3-small alongside other supported models without a separate integration for every provider.

Use the OpenAI SDK you already have — point it at Roar AI and name the model. Read the docs.

from openai import OpenAI

client = OpenAI(base_url="https://api.roar-ai.com/v1", api_key="roar_live_…")

vectors = client.embeddings.create(
    model="text-embedding-3-small",
    input=["Quarterly sales report, western province"],
)

Requests to text-embedding-3-small are processed on international infrastructure, outside India. The model directory says where each model is processed.

text-embedding-3-small vs text-embedding-3-largeHead to head

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