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Claude Opus 5 AI Chat Playground et API

Essayez Claude Opus 5 en ligne pour le raisonnement complexe, les tâches de codage exigeantes et l’analyse en plusieurs étapes. Comparez les tarifs des jetons en direct et intégrez le même modèle à votre flux de travail API.

EntréeOfficiel $5.00 par million de tokensAIReiter $2.50 par million de tokensSortieOfficiel $25.00 par million de tokensAIReiter $12.50 par million de tokensLecture cacheOfficiel $0.50 par million de tokensAIReiter $0.25 par million de tokensCréation cacheOfficiel $6.25 par million de tokensAIReiter $3.13 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-opus-5:

const message = await client.messages.create({
    "model": "claude-opus-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-opus-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-opus-5:

message = client.messages.create(
      model = "claude-opus-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-opus-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-opus-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-opus-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-opus-5",
  "input": {
    "model": "claude-opus-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-opus-5
Fournisseur
Anthropic
Protocole
Anthropic Messages
Fenêtre de contexte
1,000,000 tokens
Sortie maximale
128,000 tokens
Token d’entrée
250 crédits / 1 M de tokens
Token de sortie
1,250 crédits / 1 M de tokens
Lecture du cache
25 crédits / 1 M de tokens
Écriture du cache
312.5 crédits / 1 M de tokens

Ce que vous pouvez faire avec Claude Opus 5

Choisissez Claude Opus 5 lorsque la tâche exige un raisonnement soutenu, des décisions réfléchies et un résultat de haute qualité sur plusieurs étapes dépendantes.

Raisonnement complexe

Prenez des décisions en tenant compte de multiples contraintes, d’éléments de preuve contradictoires et d’étapes dépendantes.

Planification d’agent

Élaborez des plans, évaluez les résultats des outils et conservez un objectif clair sur des workflows plus longs.

Revue de code à enjeux élevés

Examinez l’architecture, remontez jusqu’aux défaillances subtiles et proposez des changements avec des arbitrages explicites.

Synthèse exécutive

Transformez des contenus techniques ou business denses en recommandations et plans d’action défendables.

Cas d’usage de Claude Opus 5

Idéal pour les erreurs coûteuses, les dépendances complexes et les travaux où la qualité de la décision finale compte plus que le temps de réponse le plus court.
01

Décisions d’architecture

Comparez les trajectoires de migration et mettez en évidence les risques avant d’engager du temps d’ingénierie.

02

Analyse d’incident complexe

Reliez les éléments de preuve entre les logs, le code et le contexte opérationnel.

03

Orchestration d’agents

Planifiez et examinez des workflows avec plusieurs outils ou étapes dépendantes.

04

Recherche stratégique

Synthétisez des contenus denses en décisions qui peuvent être défendues.

Comment utiliser Claude Opus 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.

Développez avec l’API Claude Opus 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 Opus 5

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

/ 01

Quand devrais-je choisir Claude Opus 5 ?

Choisissez-le pour des travaux complexes en plusieurs étapes, où la qualité du raisonnement et la prudence dans les décisions justifient un modèle phare.

/ 02

Claude Opus 5 convient-il aux agents de codage ?

Utilisez le playground pour tester la planification, la revue de code et les workflows pilotés par des outils sur les tâches de votre propre dépôt.

/ 03

Comment Claude Opus 5 est-il tarifé ?

Les tarifs des jetons d’entrée et de sortie sont affichés au-dessus du playground et doivent être vérifiés avant le routage en production.

/ 04

Chaque requête doit-elle utiliser Claude Opus 5 ?

Non. Acheminez les tâches routinières ou sensibles à la latence vers un modèle plus léger et réservez Opus 5 aux requêtes difficiles.

/ 05

Puis-je accéder à Claude Opus 5 via une API ?

Oui. Utilisez la documentation de l'API liée sur cette page et envoyez l'identifiant du modèle affiché.

AIREITER

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