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API DOCSPRICING
TEMPLATES
openaiText Chat

GPT-5.6 Terra AI Chat Playground and API

Try GPT-5.6 Terra online for balanced production coding, analysis, and automation. Test prompts, monitor token usage, and connect through the API.

InputOfficial $2.50 per 1M tokensAIReiter $1.00 per 1M tokensOutputOfficial $15.00 per 1M tokensAIReiter $6.00 per 1M tokensCache readOfficial $0.25 per 1M tokensAIReiter $0.10 per 1M tokensCache creationOfficial $3.13 per 1M tokensAIReiter $1.25 per 1M tokens
Run with 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",
    "verbosity": "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",
    "verbosity": "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",
      verbosity = "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",
      verbosity = "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",
  "verbosity": "medium"
}'

Add "stream": true to the body to receive the response as server-sent events.

OUTPUT

Example

A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers 429 Too Many Requests instead.

Handling 429

  1. Read the Retry-After response header. When present it tells you exactly how long to wait, in seconds.
  2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.
  3. Cap the number of retries, then surface the failure instead of looping forever.
async function withRetry(request, maxRetries = 4) {
  for (let attempt = 0; ; attempt++) {
    const response = await request();
    if (response.status !== 429 || attempt === maxRetries) return response;
    const retryAfter = Number(response.headers.get("retry-after"));
    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;
    await new Promise((resolve) => setTimeout(resolve, backoff));
  }
}

Treat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.

{
  "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",
    "verbosity": "medium"
  },
  "output": "A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers **429 Too Many Requests** instead.\n\n## Handling 429\n\n1. Read the `Retry-After` response header. When present it tells you exactly how long to wait, in seconds.\n2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.\n3. Cap the number of retries, then surface the failure instead of looping forever.\n\n```js\nasync function withRetry(request, maxRetries = 4) {\n  for (let attempt = 0; ; attempt++) {\n    const response = await request();\n    if (response.status !== 429 || attempt === maxRetries) return response;\n    const retryAfter = Number(response.headers.get(\"retry-after\"));\n    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;\n    await new Promise((resolve) => setTimeout(resolve, backoff));\n  }\n}\n```\n\nTreat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.",
  "metrics": {
    "input_tokens": 26,
    "output_tokens": 214,
    "generated_in_seconds": 4.1
  },
  "example": true
}
Generated in
4.1 seconds
Input tokens
26
Output tokens
214
Tokens per second
52.20 tokens / second
Time to first token
-

Model details

Use the same model key in Playground, API requests, and internal workflows.

Model ID
gpt-5.6-terra
Provider
openai
Protocol
OpenAI Chat Completions
Context window
-
Max output
-
Input tokens
100 credits / 1M tokens
Output tokens
600 credits / 1M tokens
Cache read
10 credits / 1M tokens
Cache write
125 credits / 1M tokens

What You Can Do with GPT-5.6 Terra

Choose GPT-5.6 Terra when you need a practical balance of capability, responsiveness, and cost for production workloads.

Balanced Coding

Handle feature work, debugging, tests, and code explanation with production-oriented quality.

Operational Analysis

Turn logs, reports, and requirements into structured findings and next actions.

Workflow Automation

Support agents and backend processes that need more reasoning than a lightweight tier.

Business Writing

Create clear specifications, briefs, and customer-facing content at a balanced cost.

GPT-5.6 Terra Use Cases

Best suited to production defaults that need a better capability-to-cost balance than always using the flagship tier.
01

Production Coding

Use as a practical default for feature and maintenance work.

02

Analysis Pipelines

Extract findings and recommendations from operational material.

03

Business Assistants

Support recurring writing, planning, and knowledge tasks.

04

Mid-Tier Routing

Handle work that exceeds Luna without paying for Sol on every request.

How to Use GPT-5.6 Terra

Test the model in three straightforward steps.

01

Choose Your Settings

Set the response controls and upload options supported by the model.

02

Send a Prompt

Describe the task, add relevant context, and review the streamed response and token usage.

03

Connect the API

Use the documented endpoint and your API key to bring the same model into your product.

Build with the GPT-5.6 Terra API

Go from an interactive test to a production integration with predictable controls and usage reporting.

Familiar Protocols

Use the API protocol configured for this model, including streaming where available.

Usage Visibility

Track input tokens, output tokens, and consumed credits after each response.

Model-Specific Controls

Pass the supported generation parameters instead of relying on generic defaults.

One Account and Balance

Test and operate supported text models through the same AIReiter account and billing system.

GPT-5.6 Terra FAQ

Common questions about the online playground, pricing, and API access.

/ 01

When should I choose GPT-5.6 Terra?

Choose Terra as a balanced default for production coding, analysis, writing, and automation.

/ 02

How does Terra differ from Sol and Luna?

Terra sits between flagship Sol and economical Luna, making it useful for capability-to-cost routing.

/ 03

Can Terra be used as an application default?

Yes, but validate it on your real workload and escalate only the requests that need Sol.

/ 04

How is GPT-5.6 Terra priced?

Current input and output token rates are displayed above the playground.

/ 05

Can I call GPT-5.6 Terra through an API?

Yes. Follow the linked API documentation and use model ID gpt-5.6-terra.

AIREITER

Questions? Contact us at
support@aireiter.com

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