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API DOCSPRICING
TEMPLATES
openaiText Chat

GPT-5.5 AI Chat Playground and API

Try GPT-5.5 online as a dependable general-purpose model for coding, structured writing, analysis, and API-based text workflows.

InputOfficial $5.00 per 1M tokensAIReiter $0.80 per 1M tokensOutputOfficial $30.00 per 1M tokensAIReiter $4.80 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-5.5:

const response = await client.chat.completions.create({
    "model": "gpt-5.5",
    "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-5.5",
    "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-5.5:

response = client.chat.completions.create(
      model = "gpt-5.5",
      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-5.5",
      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-5.5 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-5.5",
  "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-5.5",
  "input": {
    "model": "gpt-5.5",
    "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-5.5
Provider
openai
Protocol
OpenAI Chat Completions
Context window
-
Max output
-
Input tokens
80 credits / 1M tokens
Output tokens
480 credits / 1M tokens
Cache read
-
Cache write
-

What You Can Do with GPT-5.5

Choose GPT-5.5 as a stable general-purpose baseline for applications that mix coding, writing, analysis, and structured output.

General-Purpose Coding

Draft functions, explain errors, write tests, and assist with routine implementation work.

Structured Reasoning

Break down questions, compare alternatives, and return organized conclusions.

Reliable Writing

Create and revise documentation, business content, and concise summaries.

Application Baseline

Use one dependable model as a baseline before routing harder or cheaper tasks elsewhere.

GPT-5.5 Use Cases

Best suited to teams that want a consistent default model before introducing task-specific routing.
01

Default Application Model

Start with a consistent general model before adding routing.

02

Developer Assistance

Support routine coding, tests, and explanations.

03

Business Documents

Draft and revise clear operational content.

04

Evaluation Baseline

Compare newer or specialized models against a stable reference.

How to Use GPT-5.5

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-5.5 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-5.5 FAQ

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

/ 01

What is GPT-5.5 best for?

Use it as a dependable general-purpose model for coding, analysis, structured writing, and application baselines.

/ 02

Why keep GPT-5.5 in a multi-model stack?

A stable baseline makes it easier to measure whether a newer, cheaper, or specialized model improves your workload.

/ 03

Does GPT-5.5 support streaming here?

Yes. The playground streams responses and displays usage after completion.

/ 04

How is GPT-5.5 priced?

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

/ 05

Can I call GPT-5.5 through an API?

Yes. Follow the linked API documentation and use model ID gpt-5.5.

AIREITER

Questions? Contact us at
support@aireiter.com

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