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Google

Gemini 2.5 Flash

−30% vs Google
✓ Official API· served byGoogle Vertex AIVertex AI
Try in Playground

Gemini 2.5 Flash is Google's best price-performance model in the Gemini 2.5 family, offering well-rounded capabilities. It is recommended for large scale processing, low-latency, high-volume tasks that require thinking, and agentic use cases. It accepts text, images, video and audio, and returns text.

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 page

Price

input / output · per 1M tokens

Google Vertex AIVertex AI

−30%Only on FlintBeam

−30% on every request

Direct on Vertex AI: $0.30 / $2.50

Daily discount until 2026-11-30; it can change or end earlier.

$0.30 / $2.50$0.21 / $1.75
More pricing details: speeds
SpeedVertex AI
Priority · faster$0.54 / $4.50$0.38 / $3.15

Context

1,048,576

up to 65,536 out

65,536 by default here

Modalities

ToolsReasoning

Released

Jun 2025

Knows up to Jan 2025

What it would cost you

1000 tokens is roughly 750 English words.

Estimated total$1.29

How it is served

Standard

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

Model:google/gemini-2.5-flash
main.py
OpenAI Specification v1.0
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-2.5-flash",
    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)