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openrouter/router/openai/v1/embeddings

Generate text embeddings using OpenAI-compatible API. Access embedding models like text-embedding-3-small, text-embedding-3-large (OpenAI), and other embedding models available through OpenRouter. Drop-in replacement for the OpenAI embeddings API. Powered by OpenRouter.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

Input

Result

Idle

Waiting for your input...

You will be charged based on the number of input and output tokens.

Logs

🧩 Usage with OpenAI Client (Embeddings API)

python
from openai import OpenAI
import os

client = OpenAI(
    base_url="https://fal.run/openrouter/router/openai/v1",
    api_key="not-needed",
    default_headers={
        "Authorization": f"Key {os.environ['FAL_KEY']}",
    },
)

response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="An AI that learns to dream in colors humanity has never seen."
)

embedding = response.data[0].embedding
print("Embedding length:", len(embedding))

# Multiple texts example (batch encoding)
texts = [
    "An AI that learns to dream.",
    "A robot that remembers forgotten memories.",
    "A neural network that writes its own mythology.",
]

batch_response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input=texts,
)

for i, item in enumerate(batch_response.data):
    print(f"Text {i} embedding length:", len(item.embedding))

🧪 Comparing Embeddings (Simple Similarity Example)

python
from openai import OpenAI
import os
import math

client = OpenAI(
    base_url="https://fal.run/openrouter/router/openai/v1",
    api_key="not-needed",
    default_headers={
        "Authorization": f"Key {os.environ['FAL_KEY']}",
    },
)

def cosine_sim(a, b):
    dot = sum(x * y for x, y in zip(a, b))
    na = math.sqrt(sum(x * x for x in a))
    nb = math.sqrt(sum(x * x for x in b))
    return dot / (na * nb)

texts = [
    "An AI that learns to dream.",
    "A machine that hallucinates new worlds.",
    "A recipe for chocolate cake."
]

resp = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input=texts,
)

emb_ai = resp.data[0].embedding
emb_machine = resp.data[1].embedding
emb_recipe = resp.data[2].embedding

print("AI vs machine:", cosine_sim(emb_ai, emb_machine))
print("AI vs recipe:", cosine_sim(emb_ai, emb_recipe))

📚 Documentation

For more details, visit the official docs: