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Claude Sonnet 5 AI Chat Playground et API

Essayez Claude Sonnet 5 en ligne pour un équilibre entre codage, analyse, rédaction et assistants de production. Testez les réponses en streaming, inspectez l’utilisation des jetons et intégrez l’API.

EntréeOfficiel $2.00 par million de tokensAIReiter $1.00 par million de tokensSortieOfficiel $10.00 par million de tokensAIReiter $5.00 par million de tokensLecture cacheOfficiel $0.20 par million de tokensAIReiter $0.10 par million de tokensCréation cacheOfficiel $2.50 par million de tokensAIReiter $1.25 par million de tokens
Type de modèle
Exécuter avec l'API
PlaygroundReadmeAPI

ENTRÉE

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
Let the model reason before answering. The model decides how much thinking each request needs.Default: false
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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

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

Point the client at AIReiter:

import Anthropic from "@anthropic-ai/sdk";

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

Run claude-sonnet-5:

const message = await client.messages.create({
    "model": "claude-sonnet-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "claude-sonnet-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

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

pip install anthropic

Set the AIREITER_API_KEY environment variable:

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

Point the client at AIReiter:

import os
import anthropic

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

Run claude-sonnet-5:

message = client.messages.create(
      model = "claude-sonnet-5",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "claude-sonnet-5",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

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

Run claude-sonnet-5 against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "claude-sonnet-5",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "output_config": {
    "effort": "medium"
  }
}'

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": "claude-sonnet-5",
  "input": {
    "model": "claude-sonnet-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  },
  "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
claude-sonnet-5
Fournisseur
Anthropic
Protocole
Anthropic Messages
Fenêtre de contexte
1,000,000 tokens
Sortie maximale
128,000 tokens
Token d’entrée
100 crédits / 1 M de tokens
Token de sortie
500 crédits / 1 M de tokens
Lecture du cache
10 crédits / 1 M de tokens
Écriture du cache
125 crédits / 1 M de tokens

Ce que vous pouvez faire avec Claude Sonnet 5

Choisissez Claude Sonnet 5 comme modèle de production équilibré pour les équipes qui ont besoin d’une bonne qualité au quotidien sans envoyer chaque requête vers le niveau de tarification le plus élevé.

Codage de production

Implémentez des fonctionnalités, expliquez du code peu familier et itérez sur les corrections au cours du développement quotidien.

Analyse équilibrée

Comparez les options et résumez les éléments de preuve sans la complexité d’un flux de travail réservé au modèle phare.

Assistants clients

Alimentez des assistants utiles pour le support et en interne, qui nécessitent des réponses claires et bien structurées.

Contenu structuré

Produisez des spécifications, des briefs, des notes de version et une documentation opérationnelle réutilisable.

Cas d’utilisation de Claude Sonnet 5

Particulièrement adapté au large éventail des travaux de production : suffisamment performant pour des tâches exigeantes et suffisamment pratique pour un usage répété.
01

Développement produit

Passez du code aux tests, à la documentation et aux décisions d’implémentation.

02

Assistants de connaissance interne

Répondez aux questions opérationnelles dans un format clair et utile.

03

Automatisation du support

Rédigez des réponses précises et faites remonter les cas ambigus.

04

Production de contenu

Créez des brouillons structurés et réutilisables pour les besoins métier du quotidien.

Comment utiliser Claude Sonnet 5

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.

Créez avec l’API de Claude Sonnet 5

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 sur Claude Sonnet 5

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

/ 01

Quand dois-je choisir Claude Sonnet 5 ?

Choisissez-le comme modèle de production équilibré pour le codage, l’analyse, les assistants et la rédaction structurée.

/ 02

En quoi Sonnet 5 diffère-t-il des niveaux Opus ?

Sonnet est le choix par défaut pratique lorsque vous avez besoin d’une forte capacité au quotidien sans diriger chaque tâche vers un niveau phare.

/ 03

Claude Sonnet 5 peut-il diffuser les réponses en streaming ?

Oui. Le playground diffuse la sortie en streaming et indique l’utilisation après l’achèvement.

/ 04

Comment Claude Sonnet 5 est-il tarifé ?

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

/ 05

Puis-je intégrer Claude Sonnet 5 via une API ?

Oui. Ouvrez la documentation API liée et envoyez l'ID du modèle affiché.

AIREITER

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