Gemini Embedding 001 is Google's embedding model, released 2025-10, built for text retrieval and semantic search across technical and professional domains. It has held a top position on the MTEB Multilingual leaderboard since its experimental launch.
Modalities
inTextoutVector
Price
$0.17/ 1M in
Context
20K
Released
Oct 31, 2025
Channels
Every channel serves the same model, but they differ in price and stability. On each API key in the Platform, you pick a primary and a backup channel per vendor, and each billing line shows which channel served the request.
Provider
Discount
Input /M
Cache read /M
Officialstable
—
$0.17
—
Context pricing
This model’s price depends on how much input context a request carries. The headline price is the cheapest band, and a longer prompt moves the whole request into a higher one.
Input context
Input /M
Output /M
standardBase
$0.17
$0.00
Quick start
POST /v1/embeddingsimport os from openai import OpenAI client = OpenAI( base_url="https://api.mirapi.ai/v1", # changed api_key=os.environ["MIRAPI_API_KEY"], # changed ) resp = client.embeddings.create( model="gemini-embedding-001", input="The quick brown fox", ) vector = resp.data[0].embedding # len(vector) — pass dimensions= to shorten it
import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://api.mirapi.ai/v1", // changed apiKey: process.env.MIRAPI_API_KEY, // changed }); const resp = await client.embeddings.create({ model: "gemini-embedding-001", input: "The quick brown fox", }); const vector = resp.data[0].embedding;
curl https://api.mirapi.ai/v1/embeddings \ -H "Authorization: Bearer $MIRAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gemini-embedding-001", "input": "The quick brown fox" }'
Links
Wire Gemini Embedding 001 into your product
Start free1M free tokens to start, no card required. It’s OpenAI-compatible, so you only change the base URL.