GPT-5.6 API Key Setup: First Responses API Request

A GPT-5.6 API key is only one part of a working integration. You also need the right provider endpoint, an exact GPT-5.6 model ID, and a request format that matches the route you are calling. A key can be valid while the request still fails because it uses the wrong path, an outdated model name, or the Chat Completions shape on a route that recommends Responses.

This guide shows a complete first-request path through OurToken. You will create or locate an API key, configure the GPT-5.6 API endpoint, choose Sol, Terra, or Luna, and send the same request with cURL and Python. The working examples use the Responses API at https://api.ourtoken.ai/v1/responses.

The examples are intentionally small. First prove authentication, routing, and model selection with a short text request. Then add reasoning controls, multi-turn state, tools, retries, and usage tracking one change at a time. That sequence makes failures easier to diagnose and prevents a production application from hiding a configuration problem behind a complex prompt.

This article uses the public OurToken GPT-5.6 model documentation as the provider reference. Model availability, prices, limits, and supported parameters can change, so confirm the live model page before deploying.

How to Get a GPT-5.6 API Key

An API key is a credential for programmatic requests. It is different from a ChatGPT subscription login and it is different from a model ID. The key identifies the account or provider route that should receive the request; the model ID tells that route which GPT-5.6 model to run.

For the OurToken flow, open the OurToken API Keys page, sign in when prompted, create a key, and copy it into your local secret manager or environment. The page may redirect unauthenticated visitors to the login screen because key management is protected. That redirect does not mean the API key route is unavailable.

Separate the five values in your configuration

Keep these values separate in application configuration:

The API key is secret. The base URL and model ID are not secrets, but they should still live in configuration rather than being scattered throughout application code. This makes it possible to change from Terra to Sol or Luna without editing every request call.

Set the key in your shell before testing. These examples use an intentionally fake placeholder; do not paste a real key into a blog post, commit, browser bundle, issue report, or screenshot.

On macOS or Linux:

export OURTOKEN_API_KEY="paste-your-key-locally"

On Windows PowerShell:

$env:OURTOKEN_API_KEY = "paste-your-key-locally"

The environment variable name is local to your application. OurToken receives the credential through the HTTP Authorization: Bearer header. A command-line client or SDK adds that header for you when configured correctly.

What an API key does not prove

A key only proves that the request is authenticated. It does not prove that:

• the selected model ID exists on the route;
• the endpoint supports the fields in your request;
• the request uses the correct wire format;
• the account has enough balance for the request;
• the application handles rate limits and incomplete responses.

That is why the first smoke test should contain one model, one short input, and a conservative output limit. If it succeeds, add your real prompt and measure usage separately.

GPT-5.6 API Endpoint and Model ID

The GPT-5.6 API endpoint has two pieces: a base URL and an operation path. For the OurToken GPT-5.6 route, use:

Base URL:   https://api.ourtoken.ai/v1
Operation:  /responses
Full URL:   https://api.ourtoken.ai/v1/responses

The GPT-5.6 Terra API page currently exposes this Responses route, the gpt-5.6-terra model ID, and examples for cURL, JavaScript, Go, Python, Java, and C#. The GPT-5.6 Sol route and GPT-5.6 Luna route use the same endpoint shape with their own model IDs.

GPT-5.6 model ID selection

Use the exact model ID shown by the provider:

The official GPT-5.6 model guidance uses the same family split and states that the gpt-5.6 alias routes to gpt-5.6-sol on the OpenAI API. An aggregator or compatible provider may choose to expose only explicit IDs, so gpt-5.6-terra is a safer starting value when the provider page lists that exact route.

Do not silently replace gpt-5.6-terra with gpt-5.6, an unverified Pro alias, or a friendly display label. A model selector in a user interface can show "GPT-5.6 Terra", but the request should send the exact identifier accepted by the API.

Request architecture

The request path is easier to reason about when each boundary is explicit:

Your application
      |
      | Authorization: Bearer API_KEY
      v
https://api.ourtoken.ai/v1/responses
      |
      | model = gpt-5.6-terra
      v
GPT-5.6 route
      |
      v
Response object + usage data

For direct HTTP, call the full URL. For an OpenAI-compatible SDK, set the base URL to https://api.ourtoken.ai/v1 and call responses.create. Do not set the SDK base URL to https://api.ourtoken.ai/v1/responses unless that SDK explicitly documents a full-operation URL. Most SDKs append the operation path themselves, which would otherwise create a duplicated path.

GPT-5.6 is designed around the Responses API for reasoning, tools, and multi-turn workflows. The Responses API migration guide explains the broader difference between Chat Completions and Responses. The official Responses API migration guide explains the request and response transition.

GPT-5.6 API cURL Example

The fastest way to test a new GPT-5.6 API key is a direct cURL request. It removes SDK version differences from the first diagnostic and shows the exact URL, headers, model ID, and JSON body.

A cross-platform request shape

On macOS or Linux, run:

curl https://api.ourtoken.ai/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OURTOKEN_API_KEY" \
  -d '{
    "model": "gpt-5.6-terra",
    "input": "Give me a three-item checklist for reviewing a database migration.",
    "max_output_tokens": 256
  }'

On Windows PowerShell, use curl.exe so the command resolves to cURL rather than a PowerShell web-request alias:

curl.exe "https://api.ourtoken.ai/v1/responses" -H "Content-Type: application/json" -H "Authorization: Bearer $env:OURTOKEN_API_KEY" -d '{"model":"gpt-5.6-terra","input":"Give me a three-item checklist for reviewing a database migration.","max_output_tokens":256}'

This is the core gpt 5.6 api curl example: the key is sent in the authorization header, the endpoint ends in /responses, and the body contains the exact gpt-5.6-terra model ID. Replace only the model value when you want to compare Sol and Luna. Keep the input short during the first test so a failure is attributable to configuration rather than prompt size.

What to inspect in the response

A successful response should contain a response ID, a completed or progressing status, an output item, and usage information when the provider returns it. The exact JSON can vary by SDK and route version, but the important diagnostic questions are:

1. Did the server return HTTP 200?
2. Does the response identify the requested model or route?
3. Is there an assistant output item?
4. Did the response include usage or an actionable error object?

For a one-off diagnostic, save the response locally without logging the authorization header. For a service, record the response ID, status, latency, model ID, input-token count, output-token count, and error class. Never record the full API key.

Test a different model without changing the route

Once Terra works, repeat the same request with Sol or Luna:

curl https://api.ourtoken.ai/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OURTOKEN_API_KEY" \
  -d '{
    "model": "gpt-5.6-sol",
    "input": "Review this release checklist and identify the highest-risk missing step.",
    "max_output_tokens": 256
  }'

The route stays the same. Only the model ID and workload should change. This makes a controlled comparison possible. Do not change the endpoint, SDK, prompt, and model all at once, because you will not know which change caused a different result.

GPT-5.6 Python API Usage

The OpenAI Python SDK can send a Responses request through a compatible base URL. Install or update the SDK in the environment that runs your application:

python -m pip install --upgrade openai

Then configure the client with the OurToken API root. The usage response needs one compatibility step because the current OurToken model reference names the input and output fields prompt_tokens and completion_tokens, while the official Responses schema names them input_tokens and output_tokens:

import os

from openai import OpenAI


client = OpenAI(
    api_key=os.environ["OURTOKEN_API_KEY"],
    base_url="https://api.ourtoken.ai/v1",
)

response = client.responses.create(
    model="gpt-5.6-terra",
    input="Give me a three-item checklist for reviewing a database migration.",
    max_output_tokens=256,
)

print(response.output_text)
print(response.id)

def usage_value(usage, *field_names):
    for field_name in field_names:
        value = getattr(usage, field_name, None)
        if value is not None:
            return value
    return "unavailable"


if response.usage:
    print("input_tokens:", usage_value(response.usage, "prompt_tokens", "input_tokens"))
    print("output_tokens:", usage_value(response.usage, "completion_tokens", "output_tokens"))
    print("total_tokens:", usage_value(response.usage, "total_tokens"))

This gpt 5.6 python api usage example uses the same four values as the cURL request: the environment key, the OurToken base URL, the Responses operation selected by client.responses.create, and the explicit Terra model ID. The helper checks the field names documented by OurToken first and the official Responses names second, so the example does not assume that the provider SDK normalizes usage automatically. If it prints unavailable, inspect the raw JSON response and confirm the route's current usage schema before adding billing logic.

The SDK's output_text convenience property is useful for simple text responses. For tool calls, structured output, or applications that need every response item, inspect response.output and handle each item type explicitly. Do not assume that every response is a single plain-text message.

Continue a multi-turn response

The current OurToken GPT-5.6 model reference lists previous_response_id as an optional request field. This is a conditional compatibility test, not part of the initial smoke test. Run it only after the minimal Terra request succeeds and the selected route confirms stored response continuation. If the route returns an unsupported-parameter or missing-response error, replay the conversation history manually instead:

first = client.responses.create(
    model="gpt-5.6-terra",
    input="List the three highest-risk areas in this deployment plan.",
    max_output_tokens=256,
)

second = client.responses.create(
    model="gpt-5.6-terra",
    previous_response_id=first.id,
    input="Now turn those risks into a release-blocking checklist.",
    max_output_tokens=256,
)

print(second.output_text)

Treat state as an explicit product decision. Stored response continuation can make a workflow simpler, but it also affects retention, observability, and privacy design. If your deployment requires stateless handling, send the required history yourself and follow the provider's data controls. The official reasoning guide describes how previous_response_id and reasoning items interact in multi-turn workflows.

Set reasoning effort deliberately

The current OurToken model reference lists a reasoning object, but support is model- and route-specific. Treat the following as a conditional compatibility test after the minimal request succeeds; do not make it part of the first authentication check:

response = client.responses.create(
    model="gpt-5.6-terra",
    input="Find the most likely failure mode in this deployment plan.",
    reasoning={"effort": "medium"},
    max_output_tokens=512,
)

print(response.output_text)

Do not assume that higher effort is always better. Compare task success, latency, output tokens, and cost on representative requests. A balanced production default is often more useful than forcing the maximum setting for every request.

From First Request to Production

A successful smoke test proves that one request works. Production readiness requires a small layer around that request so that credentials, model choices, cost, and failures remain observable.

Put provider settings behind configuration

Keep provider-specific values in one configuration object:

import os


MODEL_ID = os.getenv("GPT56_MODEL_ID", "gpt-5.6-terra")
BASE_URL = os.getenv("GPT56_BASE_URL", "https://api.ourtoken.ai/v1")
API_KEY = os.environ["OURTOKEN_API_KEY"]

Validate the model ID at startup or in a deployment check. A typo should produce a clear configuration error before the application sends user work. For a model picker, store an allowlist such as:

SUPPORTED_MODELS = {
    "gpt-5.6-sol",
    "gpt-5.6-terra",
    "gpt-5.6-luna",
}

Do not accept arbitrary model strings from an untrusted frontend and pass them directly to the provider. Resolve a user-facing option to a server-side model ID, then apply the permissions and budget associated with that option.

Retry the right failures

Not every error should be retried:

Cap retries. A coding agent or queue worker that retries forever can turn a temporary outage into a much larger bill. Include the model ID and request ID in internal logs so an operator can correlate a failed task without exposing the prompt or key unnecessarily.

Track token usage and cost

Usage tracking is part of API key management because a key without a budget guard is difficult to operate safely. Record at least:

• provider and model ID;
• request status and latency;
• input and output token counts;
• retry count;
• application feature or tenant;
• estimated cost.

For a simple estimate:

estimated cost =
  input_tokens / 1,000,000 * input_rate
  + output_tokens / 1,000,000 * output_rate

For example, if the live Terra page shows $0.50 per million input tokens and $3.00 per million output tokens, a request with 2,000 input tokens and 500 output tokens is estimated as:

2,000 / 1,000,000 * $0.50 = $0.00100
500 / 1,000,000 * $3.00 = $0.00150
total = $0.00250

This is an estimate for one request, not a promise of a fixed bill. Retries, reasoning tokens, caching behavior, service tiers, and route changes can affect the final amount. For a fuller token-category and scenario calculation, see the GPT-5.6 API cost calculator.

Verify before building a wrapper

Before adding a framework, test the raw cURL request and the Python SDK with the same model, input, and output limit. Then test the specific features your application needs:

1. plain text output;
2. a longer input;
3. streaming if the user interface needs incremental output;
4. tools or structured output if the workflow requires them;
5. continuation with previous_response_id if the route exposes it;
6. an intentional invalid request to confirm error handling.

The OurToken GPT-5.6 Terra page lists the route's request fields and code examples. Treat that page as the compatibility boundary for the provider route rather than assuming that every feature documented for the official OpenAI endpoint is automatically enabled through a compatible provider.

Common GPT-5.6 API Key Errors

401 Unauthorized or invalid API key

Confirm that the key belongs to the provider URL you are calling. A key for one API account is not automatically valid on another provider route. In a manual cURL request, the header must use this shape:

Authorization: Bearer YOUR_API_KEY

In an SDK, pass the raw key without adding Bearer yourself. The SDK creates the authorization header. Check for whitespace, expired or revoked keys, and the shell in which the environment variable was created. A PowerShell environment variable set in one terminal is not necessarily available in a different terminal or service process.

404 for /responses or the model

Use https://api.ourtoken.ai/v1 as the SDK base URL and https://api.ourtoken.ai/v1/responses for a direct HTTP call. Do not mix the base URL from one provider with the model ID from another. If the route returns model-not-found, copy the exact model ID from the live model page: gpt-5.6-sol, gpt-5.6-terra, or gpt-5.6-luna.

400 unsupported parameter

Start with only model, input, and max_output_tokens. Add reasoning, tools, streaming, or continuation fields one at a time. A field can be valid in the official API reference while unavailable on a compatible provider route or on a particular model snapshot. The error response should tell you which field needs attention; log that error class, but do not log the API key.

Incomplete output

Reasoning models can spend output-token budget on internal reasoning before producing visible text. If a request ends as incomplete, inspect the status and incomplete details, reserve more output capacity, reduce the input, or test a lower reasoning effort. Do not solve an incomplete-output problem by blindly increasing retries, because the original request may already have consumed tokens.

429 or repeated timeouts

Use exponential backoff with a maximum retry count, and separate rate-limit handling from invalid-request handling. Reduce concurrency only after you have confirmed that the issue is capacity or rate limiting. Also check whether your client has an overly short timeout for long reasoning requests.

Conclusion

The reliable path to a GPT-5.6 API key is straightforward: create a provider key, keep it outside source code, use the correct https://api.ourtoken.ai/v1 base URL, call /responses, and send an exact model ID. Start with Terra for a balanced smoke test, then compare Sol or Luna against the same workload instead of changing several variables at once.

When you are ready to run the examples with a real credential, create a key through the OurToken API Keys page, select the model route from the live GPT-5.6 model page, and verify the first response before connecting the key to a larger application.

Frequently Asked Questions

How do I get a GPT-5.6 API key?

Create an API key with the provider that will receive your requests. For the OurToken route, use the API Keys page, then send the key as a Bearer token to https://api.ourtoken.ai/v1/responses. A ChatGPT subscription login and an API key are separate credentials.

What is the GPT-5.6 API endpoint?

For the OurToken GPT-5.6 route, the full Responses API endpoint is https://api.ourtoken.ai/v1/responses. When using an OpenAI-compatible SDK, set base_url to https://api.ourtoken.ai/v1 and call client.responses.create.

What is the GPT-5.6 model ID?

The explicit model IDs are gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna. Use the exact ID shown on the provider model page. Do not assume that a display name or the generic gpt-5.6 alias is accepted on every compatible route.

Can I use GPT-5.6 from Python?

Yes. Install the OpenAI Python SDK, set the OurToken base URL, pass your API key from an environment variable, and call client.responses.create with a GPT-5.6 model ID. The Python example in this guide is a minimal starting point; production code should also handle timeouts, retries, incomplete responses, and usage tracking.

Should I use Chat Completions or Responses for GPT-5.6?

Use the Responses API for the GPT-5.6 OurToken route documented in this guide. It provides the request shape used by the model pages and supports the stateful, reasoning, and tool-oriented workflows that GPT-5.6 is designed for. Do not change to /chat/completions unless the specific model page documents that route and you have tested it with the exact model ID.

How do I know whether the key works?

Run the minimal cURL request first. A successful test returns an HTTP success status, a response object, an output item, and normally usage data. Then repeat the request through Python with the same model and input. Once both clients work, add application-specific fields and verify the usage record in your account dashboard.