AIREITER
DOCS APIPREÇOS
TEMPLATES
GoogleText Chat

Gemini 3.6 Flash AI Chat Playground e API

Experimente o Gemini 3.6 Flash online para assistentes responsivos, processamento rápido de conteúdo e fluxos de trabalho de API de alto volume com saída em streaming e uso de tokens visível.

EntradaOficial $0.75 por 1 milhao de tokensAIReiter $0.225 por 1 milhao de tokensSaídaOficial $3.75 por 1 milhao de tokensAIReiter $1.125 por 1 milhao de tokensLeitura de cacheOficial $0.075 por 1 milhao de tokensAIReiter $0.0225 por 1 milhao de tokens
Executar com API
PlaygroundLeia-meAPI

ENTRADA

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
1
2
3
4
5
6
7
8
9
10
11
12

Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

npm install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api/v1",
});

Run gemini-3.6-flash:

const response = await client.chat.completions.create({
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  },
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

pip install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api/v1",
)

Run gemini-3.6-flash:

response = client.chat.completions.create(
      model = "gemini-3.6-flash",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      temperature = 1,
      top_p = 1
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "gemini-3.6-flash",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      temperature = 1,
      top_p = 1,
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run gemini-3.6-flash against AIReiter's API:

curl -s -X POST \
  -H "Authorization: Bearer $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/chat/completions" \
  -d '{
  "model": "gemini-3.6-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "max_tokens": 4096,
  "temperature": 1,
  "top_p": 1
}'

Add "stream": true to the body to receive the response as server-sent events.

SAÍDA

Example

A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that "production-ready" is a property you can demonstrate, not a hope.

Here are the first five steps, with the evidence you collect at each.

Step 1 — Get it building and the baseline green (half day) Read the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker. Evidence: The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. "this touches cron jobs and payments").

Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day) Don't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written. Evidence: An annotated trace — file → function → what it does — for that slice. A written list of "conventions I must follow" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.

Step 3 — Spike the core path (day 2) Build the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know. Evidence: A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.

Step 4 — Write the contract before the code (half of day 2 / day 3) Once the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification. Evidence: A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.

Step 5 — Implement in small, verified increments (days 3–4) Replace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed). Evidence: A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.

Steps 6+ would be the things that actually make it "shipped": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say "this will work in production" with evidence behind it, instead of a guess.

{
  "model": "gemini-3.6-flash",
  "input": {
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  },
  "output": "A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that \"production-ready\" is a property you can demonstrate, not a hope.\n\nHere are the first five steps, with the evidence you collect at each.\n\n**Step 1 — Get it building and the baseline green (half day)**\nRead the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker.\n*Evidence:* The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. \"this touches cron jobs and payments\").\n\n**Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day)**\nDon't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written.\n*Evidence:* An annotated trace — file → function → what it does — for that slice. A written list of \"conventions I must follow\" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.\n\n**Step 3 — Spike the core path (day 2)**\nBuild the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know.\n*Evidence:* A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.\n\n**Step 4 — Write the contract before the code (half of day 2 / day 3)**\nOnce the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification.\n*Evidence:* A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.\n\n**Step 5 — Implement in small, verified increments (days 3–4)**\nReplace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed).\n*Evidence:* A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.\n\nSteps 6+ would be the things that actually make it \"shipped\": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say \"this will work in production\" with evidence behind it, instead of a guess.",
  "metrics": {
    "input_tokens": 134,
    "output_tokens": 2354,
    "generated_in_seconds": 42.7
  },
  "example": true
}
Generated in
42.7 seconds
Token de entrada
134
Token de saída
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Detalhes do modelo

Use a mesma chave de modelo no Playground, nas solicitações da API e nos fluxos de trabalho internos.

ID do modelo
gemini-3.6-flash
Provedor
Google
Protocolo
OpenAI Chat Completions
Janela de contexto
1,048,576 tokens
Saída máxima
65,536 tokens
Token de entrada
22.5 créditos / 1 mi de tokens
Token de saída
112.5 créditos / 1 mi de tokens
Leitura de cache
2.25 créditos / 1 mi de tokens
Gravação de cache
-

O que você pode fazer com o Gemini 3.6 Flash

Escolha o Gemini 3.6 Flash para produtos responsivos que precisam de respostas rápidas e vazão confiável em muitas solicitações.

Assistentes Responsivos

Mantenha experiências de chat interativas e de produtividade em movimento com respostas em streaming.

Processamento Rápido de Documentos

Resuma, transforme e extraia informações de textos recebidos com velocidade de produção.

Operações de Conteúdo

Gere variações, metadados, outlines e rascunhos estruturados em grandes filas.

Automação de API

Execute tarefas frequentes de texto em que a vazão previsível é tão importante quanto a qualidade da resposta.

Casos de uso do Gemini 3.6 Flash

Mais adequado para produtos interativos e de alto volume que precisam de um modelo mais forte da camada Flash.
01

Chat Interativo

Mantenha os assistentes responsivos durante turnos repetidos do usuário.

02

Pipelines de Documentos

Resuma e transforme conteúdo recebido rapidamente.

03

Operações de Marketing

Produza variações, tags e briefs em grandes lotes.

04

Backends de Automação

Processe trabalhos recorrentes de texto com vazão previsível.

Como usar o Gemini 3.6 Flash

Teste o modelo em três etapas simples.

01

Escolha Suas Configurações

Defina os controles de resposta e as opções de upload suportadas pelo modelo.

02

Envie um Prompt

Descreva a tarefa, adicione o contexto relevante e revise a resposta em streaming e o uso de tokens.

03

Conecte a API

Use o endpoint documentado e sua API key para levar o mesmo modelo ao seu produto.

Crie com a API do Gemini 3.6 Flash

Passe de um teste interativo para uma integração em produção com controles previsíveis e relatório de uso.

Protocolos familiares

Use o protocolo da API configurado para este modelo, incluindo streaming quando disponível.

Visibilidade de uso

Acompanhe tokens de entrada, tokens de saída e créditos consumidos após cada resposta.

Controles específicos do modelo

Passe os parâmetros de geração compatíveis em vez de depender de padrões genéricos.

Uma conta e saldo

Teste e opere modelos de texto compatíveis por meio da mesma conta AIReiter e do mesmo sistema de cobrança.

FAQ do Gemini 3.6 Flash

Perguntas comuns sobre o playground online, preços e acesso à API.

/ 01

Para que o Gemini 3.6 Flash é melhor?

Use-o para assistentes responsivos, processamento rápido de documentos, operações de conteúdo e tarefas frequentes de API.

/ 02

Como devo avaliar o Gemini 3.6 Flash?

Teste prompts representativos tanto para a qualidade da resposta quanto para a latência antes de direcionar tráfego de produção de alto volume.

/ 03

O Gemini 3.6 Flash faz streaming das respostas?

Sim. O playground mostra a saída em streaming à medida que ela é gerada.

/ 04

Como o Gemini 3.6 Flash é precificado?

As taxas atuais de tokens de entrada e saída são exibidas acima do playground.

/ 05

Posso acessar o Gemini 3.6 Flash por meio de uma API?

Sim. Use a documentação da API vinculada e o ID do modelo na página.

AIREITER

Dúvidas? Entre em contato em
[email protected]

新速率有限公司NEWRATE LIMITED香港九龍花園街 2-16 號好景商業中心 2304 室Room 2304, Haojing Commercial Center, 2-16 Garden Street, Kowloon, Hong Kong

LLM

GPT-6 AstraGemini 3.8 FlashClaude Fable 5.1GLM-5.3 FlashGemini 3.6 Flash

Vídeo IA

Gemini Omni 1.1 Flash ExtMiniMax H3Kling 3.0 Motion ControlKling 3.0 TurboKling 3.0

Imagem IA

GPT-Image 2.5Grok Imagine Image 2.0Midjourney V8.1Midjourney V7Z-Image Turbo

Blog

Ver Tudo →

Empresa

Política de PrivacidadeTermos de ServiçoPolítica de Reembolso

© 2026 AIReiter. Todos os direitos reservados.