Gemini Embedding 2
Our cloudGemini Embedding 2 is the first multimodal embedding model in the Gemini API, mapping text, images, video, audio, and documents into one unified embedding space across over 100 languages. Google recommends it for cross-modal semantic search, document retrieval, and recommendation systems. Upgrading from the earlier embedding model requires re-embedding all existing data.
The real Google model
Same weights, same API, same answers. We change only how you pay for it: one key for every maker, one bill, no separate account to open. Every figure on this page comes from Google’s own page — check it.
Maker's own pagePrice
per 1M tokens
Input
Output
$0.20
$0.00
Context
8,192
Modalities
Released
Apr 2026
What it would cost you
1000 tokens is roughly 750 English words.
Where this model runs
Similar models
Models built for the same kind of work, at a similar price.
We do not mark up the price of tokens. It is the same price the provider charges. We earn on the top-up fee.
Use it from your code
from openai import OpenAI
# Works with the official openai package (pip install openai)
client = OpenAI(
base_url="https://api.flintbeam.com/v1",
api_key="sk-live-your-api-key"
)
response = client.chat.completions.create(
model="gemini-embedding-2",
messages=[
{"role": "system", "content": "You are an experienced software engineer."},
{"role": "user", "content": "Describe the architecture of a distributed cache."}
],
temperature=0.7,
max_tokens=1500
)
print(response.choices[0].message.content)