text-embedding-3-large 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.

Embeddings turn text into lists of numbers that capture its meaning, so you can search by meaning rather than keywords, find similar documents, or let an assistant draw on your own files. text-embedding-3-large is OpenAI’s most accurate: 3,072 numbers per text by default, scoring 64.6% on the MTEB benchmark.

Its real advantage is outside English. On MIRACL, a multilingual search test, it scores 54.9% against 44.0% for the small version. You can also shorten its vectors to save storage — OpenAI says a 256-number version still beats its older ada-002 model.

• Search across documents in more than one language.
• Retrieval for assistants that answer from your own files.
• Grouping and classifying documents.

• Costs more and needs more storage than text-embedding-3-small, for a modest gain in English.
• Each piece of text can be at most 8,192 tokens, so long documents need splitting first.

Roar AI provides access to text-embedding-3-large 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-large 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-large",
    input=["Quarterly sales report, western province"],
)

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

text-embedding-3-large vs text-embedding-3-smallHead to head

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