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GPT-5.6 Sol AI Chat Playground e API

Prova GPT-5.6 Sol online per i flussi di lavoro di coding, ragionamento e agent più impegnativi della famiglia GPT-5.6. Confronta i prezzi dei token e integra l'API.

InputUfficiale $4.00 per 1 M di tokenAIReiter $1.20 per 1 M di tokenOutputUfficiale $20.00 per 1 M di tokenAIReiter $6.00 per 1 M di tokenLettura cacheUfficiale $0.40 per 1 M di tokenAIReiter $0.12 per 1 M di tokenCreazione cacheUfficiale $5.00 per 1 M di tokenAIReiter $1.50 per 1 M di token
Esegui con API
PlaygroundReadmeAPI

INPUT

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
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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 gpt-5.6-sol:

const response = await client.chat.completions.create({
    "model": "gpt-5.6-sol",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "reasoning_effort": "medium"
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "gpt-5.6-sol",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "reasoning_effort": "medium"
  },
  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 gpt-5.6-sol:

response = client.chat.completions.create(
      model = "gpt-5.6-sol",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      reasoning_effort = "medium"
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "gpt-5.6-sol",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      reasoning_effort = "medium",
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

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

Run gpt-5.6-sol 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": "gpt-5.6-sol",
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "max_tokens": 4096,
  "reasoning_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": "gpt-5.6-sol",
  "input": {
    "model": "gpt-5.6-sol",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "reasoning_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
gpt-5.6-sol
Provider
OpenAI
Protocollo
OpenAI Chat Completions
Finestra di contesto
1,050,000 token
Output massimo
128,000 token
Token input
120 crediti / 1 M token
Token output
600 crediti / 1 M token
Lettura cache
12 crediti / 1 M token
Scrittura cache
150 crediti / 1 M token

Cosa puoi fare con GPT-5.6 Sol

Scegli GPT-5.6 Sol per i lavori a maggiore complessità nella famiglia GPT-5.6, soprattutto per codice, ragionamento e attività agent difficili.

Ragionamento di punta

Usa il tier GPT-5.6 più potente per problemi difficili con diversi vincoli interagenti.

Lavori software complessi

Progetta, esegui il debug e revisiona sistemi in cui la semplice corrispondenza di pattern non basta.

Agent avanzati

Pianifica flussi di lavoro più lunghi, interpreta i risultati degli strumenti e recupera quando un passaggio intermedio fallisce.

Analisi tecnica approfondita

Confronta architetture, rischi e percorsi di implementazione con trade-off espliciti.

Casi d'uso di GPT-5.6 Sol

Più adatto alle richieste più difficili in uno stack GPT-5.6 instradato; usa Terra o Luna quando l'attività non richiede la profondità del modello di punta.
01

Attività di coding difficili

Usalo per architettura, debug su più file e implementazioni complesse.

02

Attività agent avanzate

Usalo quando i piani devono resistere a più chiamate agli strumenti e revisioni.

03

Analisi approfondita

Usalo per decisioni con evidenze contrastanti e trade-off importanti.

04

Livello di escalation

Instrada qui le richieste difficili dopo che un modello meno pesante non riesce a completarle in modo affidabile.

Come usare GPT-5.6 Sol

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 l'API di GPT-5.6 Sol

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 di GPT-5.6 Sol

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

/ 01

Quando dovrei scegliere GPT-5.6 Sol?

Scegli Sol per le attività di coding, ragionamento e agent più difficili della famiglia GPT-5.6.

/ 02

In cosa Sol si differenzia da Terra e Luna?

Sol è il tier di punta; Terra è il tier di produzione bilanciato, mentre Luna privilegia velocità e costo.

/ 03

Tutto il traffico GPT-5.6 dovrebbe usare Sol?

No. Usa routing e valutazioni in modo che le richieste di routine rimangano su Terra o Luna.

/ 04

Come viene prezzato GPT-5.6 Sol?

Le tariffe attuali per token di input e output sono mostrate sopra il playground.

/ 05

Posso chiamare GPT-5.6 Sol tramite un'API?

Sì. Segui la documentazione API collegata e usa l'ID modello gpt-5.6-sol.

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