Text Embedding 3 Large
OpenAIText Embedding 3 Large is OpenAI's most capable embedding model for both English and non-English tasks. Embeddings are numerical representations of text used to measure relatedness, and they are useful for search, clustering, recommendations, anomaly detection and classification. It serves the Embeddings endpoint and is the larger of the two version 3 models.
The real OpenAI model
Same weights, same API, same answers. We change only how you pay for it: one key for every maker, one bill, no separate account to open. Every figure on this page comes from OpenAI’s own page — check it.
Maker's own pagePrice
input / output · per 1M tokensOpenAI
Official price
Context
8,191
Modalities
What it would cost you
1000 tokens is roughly 750 English words.
How it is served
Requests are retried automatically if a provider is unavailable.
Similar models
Models built for the same kind of work, at a similar price.
We do not mark up the price of tokens. It is the same price the maker charges.
Use it from your code
from openai import OpenAI
# Works with the official openai package (pip install openai)
client = OpenAI(
base_url="https://api.flintbeam.com/v1",
api_key="sk-live-your-api-key"
)
response = client.chat.completions.create(
model="openai/text-embedding-3-large",
messages=[
{"role": "system", "content": "You are an experienced software engineer."},
{"role": "user", "content": "Describe the architecture of a distributed cache."}
],
temperature=0.7,
max_tokens=1500
)
print(response.choices[0].message.content)