GPT-5.5 pro

Third-party
Zero data retention

GPT-5.5 pro uses OpenAI's Responses API with built-in tools, improved reasoning, and stateful context management.

Model Info
Context Window ↗	1,000,000 tokens
Terms and License	link ↗
More information	link ↗
Zero data retention	Yes
Request formats	Responses
Pricing	View pricing in the Cloudflare dashboard ↗

Usage

const response = await env.AI.run(
  'openai/gpt-5.5-pro',
  { input: 'What are the three laws of thermodynamics?' },
)
console.log(response)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/responses \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "openai/gpt-5.5-pro",
  "input": "What are the three laws of thermodynamics?"
}'

The **three laws of thermodynamics** are:

1. **First Law — Conservation of Energy**  
   Energy cannot be created or destroyed, only transferred or transformed.  
   In thermodynamics: the change in a system’s internal energy equals heat added to the system minus work done by the system.  
   \[
   \Delta U = Q - W
   \]

2. **Second Law — Entropy Increases**  
   In any natural process, the total entropy of an isolated system tends to increase.  
   Equivalently, heat flows spontaneously from hotter objects to colder ones, and no heat engine can be 100% efficient.

3. **Third Law — Absolute Zero Limit**  
   As temperature approaches absolute zero, the entropy of a perfect crystal approaches zero.  
   It also implies that absolute zero cannot be reached by any finite physical process.

There is also a **Zeroth Law**, often stated separately: if two systems are each in thermal equilibrium with a third system, they are in thermal equilibrium with each other. This is what makes temperature well-defined.

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}

Examples

With Instructions — Using instructions to set context

const response = await env.AI.run(
  'openai/gpt-5.5-pro',
  {
    input: 'How do I read a JSON file in Python?',
    instructions: 'You are a helpful coding assistant specializing in Python.',
  },
)
console.log(response)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/responses \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "openai/gpt-5.5-pro",
  "input": "How do I read a JSON file in Python?",
  "instructions": "You are a helpful coding assistant specializing in Python."
}'

Use Python’s built-in `json` module:

```python
import json

with open("data.json", "r", encoding="utf-8") as file:
    data = json.load(file)

print(data)
```

If `data.json` contains:

```json
{
  "name": "Alice",
  "age": 30
}
```

Then `data` will be a Python dictionary:

```python
print(data["name"])  # Alice
print(data["age"])   # 30
```

You can also handle common errors:

```python
import json

try:
    with open("data.json", "r", encoding="utf-8") as file:
        data = json.load(file)

    print(data)

except FileNotFoundError:
    print("The JSON file was not found.")

except json.JSONDecodeError:
    print("The file is not valid JSON.")
```

Use `json.load(file)` for reading from a file, and `json.loads(string)` for parsing a JSON string.

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    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "Use Python’s built-in `json` module:\n\n```python\nimport json\n\nwith open(\"data.json\", \"r\", encoding=\"utf-8\") as file:\n    data = json.load(file)\n\nprint(data)\n```\n\nIf `data.json` contains:\n\n```json\n{\n  \"name\": \"Alice\",\n  \"age\": 30\n}\n```\n\nThen `data` will be a Python dictionary:\n\n```python\nprint(data[\"name\"])  # Alice\nprint(data[\"age\"])   # 30\n```\n\nYou can also handle common errors:\n\n```python\nimport json\n\ntry:\n    with open(\"data.json\", \"r\", encoding=\"utf-8\") as file:\n        data = json.load(file)\n\n    print(data)\n\nexcept FileNotFoundError:\n    print(\"The JSON file was not found.\")\n\nexcept json.JSONDecodeError:\n    print(\"The file is not valid JSON.\")\n```\n\nUse `json.load(file)` for reading from a file, and `json.loads(string)` for parsing a JSON string.",
          "type": "output_text"
        }
      ],
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    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
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  "prompt_cache_retention": "24h",
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    "summary": null
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  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": false,
  "temperature": 1,
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    "verbosity": "medium"
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    },
    "output_tokens": 387,
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      "reasoning_tokens": 170
    },
    "total_tokens": 417
  },
  "user": null
}

Multi-turn Conversation — Continuing a conversation with message array

const response = await env.AI.run(
  'openai/gpt-5.5-pro',
  {
    input: [
      {
        content: 'I need help planning a road trip from San Francisco to Los Angeles.',
        role: 'user',
      },
      {
        content:
          "I'd be happy to help! The drive is about 380 miles and takes roughly 5-6 hours. Would you like suggestions for scenic routes or interesting stops along the way?",
        role: 'assistant',
      },
      { content: 'Yes, name three good stops in one short sentence each.', role: 'user' },
    ],
    max_output_tokens: 16000,
  },
)
console.log(response)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/responses \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "openai/gpt-5.5-pro",
  "input": [
    {
      "content": "I need help planning a road trip from San Francisco to Los Angeles.",
      "role": "user"
    },
    {
      "content": "I'\''d be happy to help! The drive is about 380 miles and takes roughly 5-6 hours. Would you like suggestions for scenic routes or interesting stops along the way?",
      "role": "assistant"
    },
    {
      "content": "Yes, name three good stops in one short sentence each.",
      "role": "user"
    }
  ],
  "max_output_tokens": 16000
}'

- Monterey/Carmel is great for beaches, seafood, and a quick scenic stroll.  
- Big Sur offers dramatic ocean views, Bixby Bridge, and McWay Falls.  
- Santa Barbara is perfect for lunch, State Street, and Stearns Wharf.

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          "annotations": [],
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  "presence_penalty": 0,
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  "prompt_cache_retention": "24h",
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  "store": false,
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    "verbosity": "medium"
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  "tool_choice": "auto",
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    "output_tokens": 251,
    "output_tokens_details": {
      "reasoning_tokens": 191
    },
    "total_tokens": 329
  },
  "user": null
}

Temperature Control — Using temperature for creative responses

const response = await env.AI.run(
  'openai/gpt-5.5-pro',
  { input: 'Write a haiku about artificial intelligence', temperature: 1 },
)
console.log(response)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/responses \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "openai/gpt-5.5-pro",
  "input": "Write a haiku about artificial intelligence",
  "temperature": 1
}'

Silent circuits dream  
Learning patterns in starlight  
Dawn hums through the code

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  "prompt_cache_retention": "24h",
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}

With Reasoning — Using reasoning effort for complex problems

const response = await env.AI.run(
  'openai/gpt-5.5-pro',
  {
    input:
      'Solve this problem step by step: A train leaves Chicago at 60mph heading east. Another train leaves New York at 80mph heading west. They are 900 miles apart. When do they meet?',
    reasoning: { effort: 'medium' },
  },
)
console.log(response)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/responses \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "openai/gpt-5.5-pro",
  "input": "Solve this problem step by step: A train leaves Chicago at 60mph heading east. Another train leaves New York at 80mph heading west. They are 900 miles apart. When do they meet?",
  "reasoning": {
    "effort": "medium"
  }
}'

Assuming both trains leave at the same time:

1. Train from Chicago speed: **60 mph**
2. Train from New York speed: **80 mph**
3. Since they are moving toward each other, add their speeds:

\[
60 + 80 = 140 \text{ mph}
\]

4. They are **900 miles** apart, so time is:

\[
\text{time} = \frac{900}{140}
\]

\[
\text{time} = 6.428571\ldots \text{ hours}
\]

5. Convert the decimal part:

\[
0.428571 \times 60 \approx 25.7 \text{ minutes}
\]

So they meet after about:

\[
\boxed{6 \text{ hours } 26 \text{ minutes}}
\]

More exactly, they meet after **6 hours, 25 minutes, and 43 seconds**.

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    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "Assuming both trains leave at the same time:\n\n1. Train from Chicago speed: **60 mph**\n2. Train from New York speed: **80 mph**\n3. Since they are moving toward each other, add their speeds:\n\n\\[\n60 + 80 = 140 \\text{ mph}\n\\]\n\n4. They are **900 miles** apart, so time is:\n\n\\[\n\\text{time} = \\frac{900}{140}\n\\]\n\n\\[\n\\text{time} = 6.428571\\ldots \\text{ hours}\n\\]\n\n5. Convert the decimal part:\n\n\\[\n0.428571 \\times 60 \\approx 25.7 \\text{ minutes}\n\\]\n\nSo they meet after about:\n\n\\[\n\\boxed{6 \\text{ hours } 26 \\text{ minutes}}\n\\]\n\nMore exactly, they meet after **6 hours, 25 minutes, and 43 seconds**.",
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      ],
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    }
  ],
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}

Web Search — Letting the model use OpenAI's built-in web search tool to answer with current information

const response = await env.AI.run(
  'openai/gpt-5.5-pro',
  {
    input: 'What were the top news stories about Cloudflare this week? Summarise in three bullets.',
    max_output_tokens: 4096,
    tools: [{ type: 'web_search_preview' }],
  },
)
console.log(response)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/responses \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "openai/gpt-5.5-pro",
  "input": "What were the top news stories about Cloudflare this week? Summarise in three bullets.",
  "max_output_tokens": 4096,
  "tools": [
    {
      "type": "web_search_preview"
    }
  ]
}'

Assuming **“this week” = Jun 16–22, 2026**:

- **Cloudflare service incident:** Cloudflare reported increased error rates/latency from 13:35 UTC today, affecting Analytics, CDN/Cache and Durable Objects; it later pointed to a fiber cut in Eastern North America and said traffic engineering had mitigated most congestion/packet loss. ([cloudflarestatus.com](https://www.cloudflarestatus.com/incidents/v3yl7jqmqj51))
- **New anti-bot/privacy protocol:** Cloudflare announced work with Mozilla Firefox, Google Chrome, Microsoft Edge and Shopify on **Private Access Control Tokens (PACT)**, meant to verify legitimate humans/agents without CAPTCHAs or invasive tracking. ([cloudflare.com](https://www.cloudflare.com/press/press-releases/2026/cloudflare-collaborates-with-leading-browsers-to-develop-a-privacy-first-protocol-for-the-global-internet/))
- **AI + SASE partner push:** Cloudflare launched a **Cloudflare One Design Partner** program and **Cloudflare One Stack**, giving select partners AI-powered workflows to help customers deploy and manage Zero Trust/SASE migrations. ([cloudflare.com](https://www.cloudflare.com/press/press-releases/2026/cloudflare-launches-design-partner-designation-to-accelerate-secure-ai-and-seamless-sase-adoption/))

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          "Cloudflare latest news June 2026",
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          "Cloudflare stock news June 2026"
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Parameters

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one ofrequired

instructions

string

temperature

numberminimum: 0maximum: 2

max_output_tokens

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top_p

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boolean

▶tools[]

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object

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object

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created_at

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model

string

▶output[]

array

output_text

string

status

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▶usage{}

object

API Schemas (Raw)

Input

Output