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AnthropicText Chat

Claude Opus 5 AI Chat Playground e API

Prova Claude Opus 5 online per ragionamento complesso, attività di coding impegnative e analisi multi-step. Confronta i prezzi dei token in tempo reale e integra lo stesso modello nel tuo flusso di lavoro API.

InputUfficiale $5.00 per 1 M di tokenAIReiter $2.50 per 1 M di tokenOutputUfficiale $25.00 per 1 M di tokenAIReiter $12.50 per 1 M di tokenLettura cacheUfficiale $0.50 per 1 M di tokenAIReiter $0.25 per 1 M di tokenCreazione cacheUfficiale $6.25 per 1 M di tokenAIReiter $3.13 per 1 M di token
Tipo di modello
Esegui con API
PlaygroundReadmeAPI

INPUT

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.

OUTPUT

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 input
134
Token output
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Dettagli del modello

Usa la stessa chiave del modello nel Playground, nelle richieste API e nei flussi di lavoro interni.

ID modello
claude-opus-5
Provider
Anthropic
Protocollo
Anthropic Messages
Finestra di contesto
1,000,000 token
Output massimo
128,000 token
Token input
250 crediti / 1 M token
Token output
1,250 crediti / 1 M token
Lettura cache
25 crediti / 1 M token
Scrittura cache
312.5 crediti / 1 M token

Cosa puoi fare con Claude Opus 5

Scegli Claude Opus 5 quando l'attività richiede ragionamento prolungato, decisioni attente e output di alta qualità attraverso diversi passaggi dipendenti.

Ragionamento complesso

Affronta decisioni con vincoli multipli, prove in conflitto e passaggi dipendenti.

Pianificazione degli agenti

Redigi piani, valuta i risultati degli strumenti e mantieni un obiettivo chiaro לאורך workflows più lunghi.

Code review ad alto rischio

Esamina l'architettura, individua guasti sottili e proponi modifiche con compromessi espliciti.

Sintesi per dirigenti

Trasforma materiale tecnico o aziendale denso in raccomandazioni e piani d'azione difendibili.

Casi d'uso di Claude Opus 5

Ideale per errori costosi, dipendenze complesse e lavori in cui la qualità della decisione finale conta più del tempo di risposta più breve.
01

Decisioni architetturali

Confronta i percorsi di migrazione ed evidenzia i rischi prima di investire tempo di engineering.

02

Analisi di incidenti complessi

Collega le evidenze tra log, codice e contesto operativo.

03

Orchestrazione degli agenti

Pianifica e valuta workflow con più strumenti o fasi dipendenti.

04

Ricerca strategica

Sintetizza materiale denso in decisioni che possano essere difese.

Come usare Claude Opus 5

Prova il modello in tre semplici passaggi.

01

Scegli le impostazioni

Imposta i controlli di risposta e le opzioni di caricamento supportate dal modello.

02

Invia un prompt

Descrivi l'attività, aggiungi il contesto rilevante e rivedi la risposta in streaming e l'uso dei token.

03

Collega l'API

Usa l'endpoint documentato e la tua API key per integrare lo stesso modello nel tuo prodotto.

Crea con la API di Claude Opus 5

Passa da un test interattivo a un'integrazione in produzione con controlli prevedibili e reportistica sull'utilizzo.

Protocolli familiari

Usa il protocollo API configurato per questo modello, inclusa lo streaming dove disponibile.

Visibilità sull'utilizzo

Tieni traccia dei token di input, dei token di output e dei crediti consumati dopo ogni risposta.

Controlli specifici del modello

Passa i parametri di generazione supportati invece di affidarti a valori predefiniti generici.

Un solo account e saldo

Testa e utilizza i modelli di testo supportati tramite lo stesso account AIReiter e lo stesso sistema di fatturazione.

FAQ su Claude Opus 5

Domande frequenti sul playground online, sui prezzi e sull'accesso API.

/ 01

Quando dovrei scegliere Claude Opus 5?

Sceglilo per lavori complessi e multi-step in cui la qualità del ragionamento e decisioni accurate giustificano un modello di punta.

/ 02

Claude Opus 5 è adatto per coding agent?

Usa il playground per testare pianificazione, code review e workflow guidati da strumenti sui task del tuo repository.

/ 03

Come viene prezzato Claude Opus 5?

Le tariffe per token in input e output sono mostrate sopra il playground e vanno verificate prima del routing in produzione.

/ 04

Ogni richiesta dovrebbe usare Claude Opus 5?

No. Instrada i carichi di lavoro di routine o sensibili alla latenza verso un modello più leggero e riserva Opus 5 alle richieste difficili.

/ 05

Posso accedere a Claude Opus 5 tramite un API?

Sì. Usa la documentazione API collegata in questa pagina e invia l'ID del modello mostrato.

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