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OpenAI のロゴ

GPT-5.6 Terra

テキスト生成 • OpenAI

Markdown で表示Agent セットアップ
  • サードパーティ

GPT-5.6 Terra は、知能とコストのバランスを取った OpenAI の GPT-5.6 モデルです。Responses API で推論とステートフルなコンテキスト管理を行います。

モデル情報
コンテキストウィンドウ1,050,000 トークン
利用規約とライセンスリンク
詳細情報リンク
リクエスト形式Responses
料金Cloudflare ダッシュボードで料金を見る

使い方

const response = await env.AI.run(
  'openai/gpt-5.6-terra',
  {
    input: 'Compare blue-green and canary deployments for a small engineering team.',
    instructions: 'Answer in two concise paragraphs.',
    max_output_tokens: 512,
  },
)
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-5.6-terra",
  "input": "Compare blue-green and canary deployments for a small engineering team.",
  "instructions": "Answer in two concise paragraphs.",
  "max_output_tokens": 512
}'
Blue-green deployments run two identical production environments: one active (“blue”) and one idle (“green”). A new version is deployed and tested on green, then traffic is switched over all at once. This makes rollback very fast—switch traffic back to blue—but requires maintaining duplicate infrastructure and handling database/schema compatibility carefully. For a small team, blue-green is often appealing when releases are infrequent, the system is simple enough to duplicate cheaply, and a clear cutover/rollback procedure matters more than gradual validation.

Canary deployments release the new version to a small percentage of users or requests first, then progressively increase traffic while monitoring errors, latency, and business metrics. They reduce the blast radius of defects and provide real-production validation, but require traffic splitting, strong observability, automated rollout controls, and usually feature-flag or version-compatibility discipline. For a small team, canaries are best when the platform already supports them or when the cost of a faulty release is high; otherwise, blue-green generally offers a simpler operational model.
{
  "id": "resp_06924a0adf314715016a4fe98e1d2c81978067f843c9a357f1",
  "object": "response",
  "created_at": 1783622030,
  "status": "completed",
  "background": false,
  "billing": {
    "payer": "developer"
  },
  "completed_at": 1783622032,
  "error": null,
  "frequency_penalty": 0,
  "incomplete_details": null,
  "instructions": "Answer in two concise paragraphs.",
  "max_output_tokens": 512,
  "max_tool_calls": null,
  "model": "gpt-5.6-terra",
  "moderation": null,
  "output": [
    {
      "id": "msg_06924a0adf314715016a4fe98e76c4819780adfca3dd3ad1f3",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "Blue-green deployments run two identical production environments: one active (“blue”) and one idle (“green”). A new version is deployed and tested on green, then traffic is switched over all at once. This makes rollback very fast—switch traffic back to blue—but requires maintaining duplicate infrastructure and handling database/schema compatibility carefully. For a small team, blue-green is often appealing when releases are infrequent, the system is simple enough to duplicate cheaply, and a clear cutover/rollback procedure matters more than gradual validation.\n\nCanary deployments release the new version to a small percentage of users or requests first, then progressively increase traffic while monitoring errors, latency, and business metrics. They reduce the blast radius of defects and provide real-production validation, but require traffic splitting, strong observability, automated rollout controls, and usually feature-flag or version-compatibility discipline. For a small team, canaries are best when the platform already supports them or when the cost of a faulty release is high; otherwise, blue-green generally offers a simpler operational model."
        }
      ],
      "phase": "final_answer",
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "prompt_cache_retention": "in_memory",
  "reasoning": {
    "context": "all_turns",
    "effort": "medium",
    "mode": "standard",
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "store": false,
  "temperature": 1,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tool_usage": {
    "image_gen": {
      "input_tokens": 0,
      "input_tokens_details": {
        "image_tokens": 0,
        "text_tokens": 0
      },
      "output_tokens": 0,
      "output_tokens_details": {
        "image_tokens": 0,
        "text_tokens": 0
      },
      "total_tokens": 0
    },
    "web_search": {
      "num_requests": 0
    }
  },
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.98,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 29,
    "input_tokens_details": {
      "cache_write_tokens": 0,
      "cached_tokens": 0
    },
    "output_tokens": 211,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "total_tokens": 240
  },
  "user": null,
  "metadata": {}
}

顧客インシデントの更新 — 指示でモデルの応答スタイルを調整します
const response = await env.AI.run(
  'openai/gpt-5.6-terra',
  {
    input: 'Draft a short incident update for customers after elevated API latency.',
    instructions: 'Be transparent, calm, and avoid unnecessary technical detail.',
    max_output_tokens: 512,
  },
)
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-5.6-terra",
  "input": "Draft a short incident update for customers after elevated API latency.",
  "instructions": "Be transparent, calm, and avoid unnecessary technical detail.",
  "max_output_tokens": 512
}'
**Incident Update:**  
Earlier today, some customers experienced elevated API latency. Our team identified the issue and applied mitigation steps, and API performance has returned to normal.

We’ll continue monitoring closely and will share a follow-up with additional details if needed. We apologize for the disruption and appreciate your patience.
{
  "id": "resp_0072e6076b1b184d016a4fe99105888190b25ef699c2a83380",
  "object": "response",
  "created_at": 1783622033,
  "status": "completed",
  "background": false,
  "billing": {
    "payer": "developer"
  },
  "completed_at": 1783622034,
  "error": null,
  "frequency_penalty": 0,
  "incomplete_details": null,
  "instructions": "Be transparent, calm, and avoid unnecessary technical detail.",
  "max_output_tokens": 512,
  "max_tool_calls": null,
  "model": "gpt-5.6-terra",
  "moderation": null,
  "output": [
    {
      "id": "msg_0072e6076b1b184d016a4fe9916d6081909385f3b15352b1a7",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "**Incident Update:**  \nEarlier today, some customers experienced elevated API latency. Our team identified the issue and applied mitigation steps, and API performance has returned to normal.\n\nWe’ll continue monitoring closely and will share a follow-up with additional details if needed. We apologize for the disruption and appreciate your patience."
        }
      ],
      "phase": "final_answer",
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "prompt_cache_retention": "in_memory",
  "reasoning": {
    "context": "all_turns",
    "effort": "medium",
    "mode": "standard",
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "store": false,
  "temperature": 1,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tool_usage": {
    "image_gen": {
      "input_tokens": 0,
      "input_tokens_details": {
        "image_tokens": 0,
        "text_tokens": 0
      },
      "output_tokens": 0,
      "output_tokens_details": {
        "image_tokens": 0,
        "text_tokens": 0
      },
      "total_tokens": 0
    },
    "web_search": {
      "num_requests": 0
    }
  },
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.98,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 33,
    "input_tokens_details": {
      "cache_write_tokens": 0,
      "cached_tokens": 0
    },
    "output_tokens": 64,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "total_tokens": 97
  },
  "user": null,
  "metadata": {}
}

パラメーター

instructions
string
temperature
numberminimum: 0maximum: 2
max_output_tokens
numberexclusiveMinimum: 0
top_p
numberminimum: 0maximum: 1
stream
boolean
tool_choice
id
string
object
stringconst: response
created_at
number
model
string
output_text
string
status
stringenum: in_progress, completed, failed, incomplete

API スキーマ(Raw)

Input
Output

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