Gemini Embedding 2
−30% vs Google
Vertex AIGemini 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
input / output · per 1M tokensVertex AI
−30%Only on FlintBeam−30% on every request
Direct on Vertex AI: $0.20 / $0.00
Daily discount until 2026-11-30; it can change or end earlier.
Context
8,192
Modalities
Released
Apr 2026
What it would cost you
1000 tokens is roughly 750 English words.
How it is served
Requests are retried automatically if a provider is unavailable.
Similar models
Models built for the same kind of work, at a similar price.
Eligible models may have a daily discount below the maker's price. Daily discounts apply to eligible models and can change from day to day.
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="google/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)