ReadyIntel TDX

OpenAI: Text Embedding 3 Large

Model IDopenai/text-embedding-3-large

text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

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input

$0.13/M

output

Free/M

context

created

Nov 14, 2025

Supported API shape

input

text · embeddings

output

embeddings

tools

Not listed

json mode

Not listed

Verification

receipt

x-receipt-id

attestation

gateway report

session

attested upstream

provider

Phala

Provider

Phala

Intel TDX

input

$0.13/M

output

Free/M

context

Performance comparison

Last 72h · UTC

$ pip install openai

1from openai import OpenAI
2import os
3
4MODEL = "openai/text-embedding-3-large"
5
6client = OpenAI(
7    base_url="https://inference.phala.com/v1",
8    api_key=os.environ["PHALA_API_KEY"],
9)
10
11response = client.embeddings.create(
12    model=MODEL,
13    input="Phala keeps inference data inside a confidential runtime.",
14    extra_body={"provider": {"aci_verified": True}},
15)
16
17print(response.data[0].embedding[:8])

$ npm install openai

1import OpenAI from "openai"
2
3const MODEL = "openai/text-embedding-3-large"
4
5const openai = new OpenAI({
6  baseURL: "https://inference.phala.com/v1",
7  apiKey: process.env.PHALA_API_KEY,
8})
9
10const response = await openai.embeddings.create({
11  model: MODEL,
12  input: "Phala keeps inference data inside a confidential runtime.",
13  provider: { aci_verified: true },
14})
15
16console.log(response.data[0].embedding.slice(0, 8))

1curl https://inference.phala.com/v1/embeddings \
2  -H "Authorization: Bearer $PHALA_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "openai/text-embedding-3-large",
6    "input": "Phala keeps inference data inside a confidential runtime.",
7    "provider": {"aci_verified": true}
8  }'

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