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DeepInfra supports the OpenAI embeddings API for all embedding models. The endpoint is: 
POST https://api.deepinfra.com/v1/openai/embeddings

​
Example

from openai import OpenAI

openai = OpenAI(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/v1/openai",
)

input_text = "The food was delicious and the waiter..."
# Or a list: ["hello", "world"]

embeddings = openai.embeddings.create(
    model="Qwen/Qwen3-Embedding-8B",
    input=input_text,
    encoding_format="float"
)

print(embeddings.data[0].embedding)
print(embeddings.usage.prompt_tokens)

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://api.deepinfra.com/v1/openai",
  apiKey: "$DEEPINFRA_TOKEN",
});

const input = "The quick brown fox jumped over the lazy dog";
// Or an array: ["hello", "world"]

const embedding = await openai.embeddings.create({
  model: "Qwen/Qwen3-Embedding-8B",
  input: input,
  encoding_format: "float",
});

console.log(embedding.data[0].embedding);
console.log(embedding.usage.prompt_tokens);

curl "https://api.deepinfra.com/v1/openai/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "Qwen/Qwen3-Embedding-8B",
    "encoding_format": "float"
  }'

​
Batch embeddings

Pass an array as input to embed multiple texts in a single request: 
embeddings = openai.embeddings.create(
    model="Qwen/Qwen3-Embedding-8B",
    input=["Hello", "World", "How are you?"],
    encoding_format="float"
)

for i, item in enumerate(embeddings.data):
    print(f"Text {i}: {item.embedding[:5]}...")  # First 5 dims

​
Supported parameters

ParameterNotes
modelEmbedding model name
inputString or array of strings
encoding_formatfloat only

​
Available models

Browse all embedding models — includes Qwen3 Embedding, BAAI/bge, sentence-transformers, and more.