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Google

Gemini 3.5 Flash

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

Gemini 3.5 Flash provides sustained frontier-level intelligence optimized for real-world tasks at higher speed and lower cost. Designed for the agentic era, it excels at sub-agent deployment, multi-step workflows, and long-horizon tasks at scale. Google now lists it as a legacy Flash model for routine, high-throughput workloads. It thinks before it answers, and that thinking counts toward max_tokens: allow at least 1024 tokens for a short answer and 4096 or more (or reasoning effort low) for a longer one.

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: $1.50 / $9.00

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

$1.50 / $9.00$1.05 / $6.30
More pricing details: speeds
SpeedVertex AI
Flex · slower$0.75 / $4.50$0.60 / $3.60
Priority · faster$2.70 / $16.20$1.89 / $11.34

Context

1,048,576

up to 65,536 out

65,536 by default here

Modalities

ToolsReasoning

Released

May 19, 2026

What it would cost you

1000 tokens is roughly 750 English words.

Estimated total$5.25

How it is served

Standard

Requests are retried automatically if a provider is unavailable.

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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-3.5-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-3.5-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)