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GPT-5.6 Terra AI Chat Playground e API

Prova GPT-5.6 Terra online per coding, analisi e automazione in produzione con un equilibrio ottimale. टेस्ट prompts, monitora l'utilizzo dei token e connettiti tramite API.

InputUfficiale $2.00 per 1 M di tokenAIReiter $0.60 per 1 M di tokenOutputUfficiale $12.00 per 1 M di tokenAIReiter $3.60 per 1 M di tokenLettura cacheUfficiale $0.20 per 1 M di tokenAIReiter $0.06 per 1 M di tokenCreazione cacheUfficiale $2.50 per 1 M di tokenAIReiter $0.75 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-terra:

const response = await client.chat.completions.create({
    "model": "gpt-5.6-terra",
    "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-terra",
    "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-terra:

response = client.chat.completions.create(
      model = "gpt-5.6-terra",
      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-terra",
      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-terra 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-terra",
  "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-terra",
  "input": {
    "model": "gpt-5.6-terra",
    "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-terra
Provider
OpenAI
Protocollo
OpenAI Chat Completions
Finestra di contesto
1,050,000 token
Output massimo
128,000 token
Token input
60 crediti / 1 M token
Token output
360 crediti / 1 M token
Lettura cache
6 crediti / 1 M token
Scrittura cache
75 crediti / 1 M token

Cosa puoi fare con GPT-5.6 Terra

Scegli GPT-5.6 Terra quando ti serve un equilibrio pratico tra capacità, reattività e costo per i carichi di lavoro in produzione.

Coding equilibrato

Gestisci sviluppo di funzionalità, debugging, test e spiegazione del codice con una qualità orientata alla produzione.

Analisi operativa

Trasforma log, report e requisiti in risultati strutturati e azioni successive.

Automazione dei workflow

Supporta agenti e processi backend che richiedono più ragionamento di un tier leggero.

Scrittura aziendale

Crea specifiche chiare, brief e contenuti rivolti ai clienti a un costo equilibrato.

Casi d'uso di GPT-5.6 Terra

Più adatto ai default di produzione che richiedono un migliore equilibrio tra capacità e costo rispetto all'uso sempre del tier di punta.
01

Coding in produzione

Usalo come default pratico per il lavoro su funzionalità e manutenzione.

02

Pipeline di analisi

Estrai risultati e raccomandazioni da materiale operativo.

03

Assistenti aziendali

Supporta attività ricorrenti di scrittura, pianificazione e gestione della conoscenza.

04

Routing di fascia media

Gestisci lavori che superano Luna senza pagare Sol per ogni richiesta.

Come usare GPT-5.6 Terra

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 Terra

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 GPT-5.6 Terra

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

/ 01

Quando dovrei scegliere GPT-5.6 Terra?

Scegli Terra come default equilibrato per coding, analisi, scrittura e automazione in produzione.

/ 02

In cosa Terra differisce da Sol e Luna?

Terra si colloca tra il flagship Sol e l'economico Luna, risultando utile per il routing in base al rapporto capacità-costo.

/ 03

Terra può essere usato come default dell'applicazione?

Sì, ma validalo sul tuo workload reale ed esegui l'upgrade solo per le richieste che necessitano di Sol.

/ 04

Come viene prezzato GPT-5.6 Terra?

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

/ 05

Posso chiamare GPT-5.6 Terra tramite un'API?

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

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