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Gemini Embedding 001 API

model: gemini-embedding-001

This model is not available on the CallAI gateway yet; prices shown are the official reference. See the model directory for available models in the same family.

Embedding model for semantic search, retrieval, clustering, and ranking pipelines

CallAI pricing

Crypto OK

Input / 1M:$0.165

Output / 1M:$0

Specs & upstream

Upstream (in/out):$0.150 / $0

Context:2K

Not callable yet

Gemini Embedding 001 is not live on the CallAI gateway yet. The code below will work once it is listed; calling it now returns model_not_available. Please don't top up for this model: USDT top-ups are non-refundable once credited (details). See the model directory for available models in the same family.

SDK quickstart

OpenAI-compatible

Python

from openai import OpenAI

client = OpenAI(
    api_key="sk-live-your-callai-key",
    base_url="https://api.callaiapi.com/v1"
)

response = client.chat.completions.create(
    model="gemini-embedding-001",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Hello, explain quicksort with Python"}
    ],
    stream=True
)

for chunk in response:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

Node.js / TypeScript

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.CALLAI_API_KEY,
  baseURL: 'https://api.callaiapi.com/v1',
});

const response = await openai.chat.completions.create({
  model: 'gemini-embedding-001',
  messages: [{ role: 'user', content: 'Hello!' }],
  stream: true,
});

for await (const chunk of response) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}