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Gemini 3.6 Flash AI Chat Playground et API

Essayez Gemini 3.6 Flash en ligne pour des assistants réactifs, un traitement rapide du contenu et des workflows API à fort volume avec une sortie diffusée en continu et une utilisation visible des tokens.

EntréeOfficiel $0.75 par million de tokensAIReiter $0.225 par million de tokensSortieOfficiel $3.75 par million de tokensAIReiter $1.125 par million de tokensLecture cacheOfficiel $0.075 par million de tokensAIReiter $0.0225 par million de tokens
Exécuter avec l'API
PlaygroundReadmeAPI

ENTRÉE

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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.

SORTIE

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 d’entrée
134
Token de sortie
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Détails du modèle

Utilisez la même clé de modèle dans le Playground, les requêtes API et les workflows internes.

ID du modèle
gemini-3.6-flash
Fournisseur
Google
Protocole
OpenAI Chat Completions
Fenêtre de contexte
1,048,576 tokens
Sortie maximale
65,536 tokens
Token d’entrée
22.5 crédits / 1 M de tokens
Token de sortie
112.5 crédits / 1 M de tokens
Lecture du cache
2.25 crédits / 1 M de tokens
Écriture du cache
-

Ce que vous pouvez faire avec Gemini 3.6 Flash

Choisissez Gemini 3.6 Flash pour des produits réactifs qui nécessitent des réponses rapides et un débit fiable sur de nombreuses requêtes.

Assistants réactifs

Faites avancer les expériences de chat interactif et de productivité grâce à des réponses diffusées en continu.

Traitement rapide des documents

Résumez, transformez et extrayez des informations du texte entrant à vitesse de production.

Opérations sur le contenu

Générez des variantes, des métadonnées, des plans et des brouillons structurés sur de grandes files d’attente.

Automatisation API

Exécutez des tâches textuelles fréquentes où un débit prévisible est aussi important que la qualité des réponses.

Cas d’usage de Gemini 3.6 Flash

Particulièrement adapté aux produits interactifs et à fort débit qui nécessitent un modèle plus performant de la gamme Flash.
01

Chat interactif

Gardez les assistants réactifs pendant les échanges successifs avec les utilisateurs.

02

Chaînes de traitement de documents

Résumez et transformez rapidement le contenu entrant.

03

Opérations marketing

Produisez des variantes, des balises et des briefs sur de grands lots.

04

Backends d’automatisation

Traitez des tâches textuelles récurrentes avec un débit prévisible.

Comment utiliser Gemini 3.6 Flash

Testez le modèle en trois étapes simples.

01

Choisissez vos paramètres

Définissez les contrôles de réponse et les options d’envoi prises en charge par le modèle.

02

Envoyez une instruction

Décrivez la tâche, ajoutez le contexte pertinent, et consultez la réponse diffusée en continu ainsi que l’utilisation des tokens.

03

Connectez l’API

Utilisez le endpoint documenté et votre API key pour intégrer ce même modèle à votre produit.

Construisez avec l’API Gemini 3.6 Flash

Passez d’un test interactif à une intégration en production avec des contrôles prévisibles et un suivi de l’utilisation.

Protocoles familiers

Utilisez le protocole API configuré pour ce modèle, y compris le streaming lorsqu’il est disponible.

Visibilité de l’utilisation

Suivez les tokens d’entrée, les tokens de sortie et les crédits consommés après chaque réponse.

Contrôles spécifiques au modèle

Passez les paramètres de génération pris en charge au lieu de vous fier à des valeurs par défaut génériques.

Un seul compte et un seul solde

Testez et exploitez les modèles de texte pris en charge via le même compte AIReiter et le même système de facturation.

FAQ Gemini 3.6 Flash

Questions fréquentes sur le playground en ligne, la tarification et l’accès à l’API.

/ 01

À quoi sert le mieux Gemini 3.6 Flash ?

Utilisez-le pour des assistants réactifs, le traitement rapide de documents, les opérations sur le contenu et les tâches API fréquentes.

/ 02

Comment dois-je évaluer Gemini 3.6 Flash ?

Testez des invites représentatives pour la qualité des réponses et la latence avant d’attribuer un trafic de production à grand volume.

/ 03

Gemini 3.6 Flash diffuse-t-il les réponses en continu ?

Oui. Le playground affiche la sortie diffusée au fur et à mesure de sa génération.

/ 04

Comment Gemini 3.6 Flash est-il tarifé ?

Les tarifs actuels des tokens d’entrée et de sortie sont affichés au-dessus du playground.

/ 05

Puis-je accéder à Gemini 3.6 Flash via une API ?

Oui. Utilisez la documentation API liée et l'ID du modèle de la page.

AIREITER

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