Overview

Gemini 3 Flash Preview delivers high-throughput inference optimized for extensive context processing, supporting up to 1,000,000 tokens within a single request window. Built on the proprietary Gemini family, this FP16 quantized model balances latency and accuracy, making it ideal for real-time chat applications and large-document analysis. Developers can integrate immediately via standardized API endpoints, requiring minimal configuration to execute the first call. The architecture ensures robust performance across bilingual tasks, specifically validated for mixed Arabic and English workflows, providing researchers with reliable benchmark consistency for complex linguistic pipelines.

For production environments, this model operates under a starter tier requirement with a 3.5x credit multiplier relative to base rates, offering transparent cost predictability without hidden fees. Enterprise teams benefit from full Arabic language support, ensuring compliance with regional language standards while maintaining global performance metrics. The proprietary license guarantees stability for commercial deployments, allowing decision-makers to scale operations confidently. Whether building customer support agents or analyzing lengthy technical documentation, Gemini 3 Flash Preview provides the necessary throughput and linguistic precision to meet strict service level agreements without requiring additional sales consultations for basic usage terms.

Specifications

Display Name Gemini 3 Flash Preview

Family Gemini

Category Vision

Parameters unknown

Context Window 1,000,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

3.5×

credits per token

1K 3,500 Credits

10K 35,000 Credits

100K 350,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="gemini-3-flash-preview",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "gemini-3-flash-preview",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire codebases for security bugs and vulnerabilities

Summarizing extensive legal contracts and regulatory documents

Processing hours of video transcript data efficiently

Interactive tutoring sessions using full textbook content

Searching through massive internal knowledge base documents

In-Depth Guide

Full Guide

Complete Guide to Gemini 3 Flash Preview — LLM Resayil

Gemini 3 Flash Preview