1. OpenAI
toiotech
  • toiotech接口说明
  • 已支持的模型列表
  • 大模型官方文档
  • Claude thinking 多轮对话
  • thinking相关说明
  • 已支持的模型列表
    • 语言大模型
      • OpenAI
        • gpt-4
          POST
        • gpt-4.1
          POST
        • gpt-4o
          POST
        • gpt-4o-mini
          POST
        • gpt-5
          POST
        • gpt-5-chat
          POST
        • gpt-5-mini
          POST
        • gpt-5-nano
          POST
        • gpt-5.1
          POST
        • gpt-5.1-chat-latest
          POST
        • gpt-5.1-codex(仅支持/v1/responses)
          POST
        • gpt-5.2
          POST
        • gpt-5.2(responses)
          POST
        • gpt-5.2-chat
          POST
        • gpt-5.2-pro(仅支持/v1/responses)
          POST
        • gpt-oss-120b
          POST
        • o1
          POST
        • o3
          POST
        • o3-mini
          POST
        • o3-deep-research
          POST
        • o3-pro
          POST
        • o4-mini
          POST
        • o4-mini-deep-research
          POST
        • gpt-5.4
          POST
        • gpt-5.4-pro
          POST
      • Google
        • gemini-2.0-flash
        • gemini-2.5-computer-use-preview-10-2025
        • gemini-2.5-flash
        • gemini-2.5-flash-lite
        • gemini-2.5-flash-lite-preview
        • gemini-2.5-pro
        • gemini-3-flash-preview
        • gemini-3-pro-preview
        • gemini-robotics-er-1.5-preview
        • gemini-(thinking_config)openai请求格式
        • gemini-3.1-pro-preview(多模态)
        • gemini-3.1-flash-lite-preview(多模态)
        • gemini-3.1-flash-lite-preview(thinking)
        • gemini-2.5-pro(原生请求格式)
      • Anthropic
        • 通用接口
        • claude-opus-4-5-20251101
        • claude-opus-4-20250514
        • claude-sonnet-4-20250514
        • claude-sonnet-4-5-20250929
        • claude-(Interleaved thinking)
        • claude-opus-4-6
        • claude-sonnet-4-6
      • DeepSeek
        • deepseek-chat
        • deepseek-reasoner
      • MiniMax
        • abab6.5s-chat
        • minimax-m2
        • minimax-m2.5
      • Moonshot
        • kimi-k2
        • kimi-k2-0711-preview
        • kimi-k2-0905
        • kimi-k2-instruct
        • kimi-k2-thinking
        • moonshot-v1-128k
        • moonshot-v1-32k
        • moonshot-v1-8k
        • moonshot-v1-128k-vision-preview
        • moonshot-v1-32k-vision-preview
        • moonshot-v1-8k-vision-preview
        • kimi-k2.5
        • 获取系统工具
        • 执行系统工具
      • xAI
        • grok-4
        • grok-4-1-fast-non-reasoning
        • grok-4-1-fast-reasoning
      • 字节跳动
        • 通用接口
        • doubao-1.8图片理解
        • doubao-1.8(视频理解)
        • doubao-seed-2-0-pro-260215(图片理解)
        • doubao-seed-2-0-pro-260215(视频理解)
      • 智谱
        • glm-4-plus
        • glm-4.5
        • glm-4.6
        • glm-4.7
        • glm-5
      • 阿里巴巴
        • qwen-max
        • qwen-max-2025-01-25
        • qwen-max-latest
        • qwen-plus
        • qwen-plus-latest
        • qwen-turbo
        • qwen2.5-7b-instruct
        • qwen2.5-32b-instruct
        • qwen2.5-72b-instruct
        • qwen3-coder-plus
        • qwen3-coder-plus-2025-07-22
        • qwen3-max
        • qwen3-max-preview
        • qwen3-235b-a22b
        • qwen3-235b-a22b-instruct-2507
        • qwen3-235b-a22b-thinking-2507
        • qwen3-8b
        • qwen3-coder-30b-a3b-instruct
        • qwen3-next-80b-a3b-instruct
        • qwen3-next-80b-a3b-thinking
        • qwen-vl-max(视觉理解)
        • qwen-vl-plus(视觉理解)
        • qwen3-vl-235b-a22b-instruct(视觉理解)
        • qwen3-vl-235b-a22b-thinking(视觉理解)
        • qwen3-vl-30b-a3b-thinking(图片理解)
        • qwen3-vl-30b-a3b-thinking(视频理解)
        • qwen3-max-2026-01-23(qwen3-max-thinking)
        • qwen3-max-2026-01-23(非思考模式)
        • qwen3.5-397b-a17b(多模态)
        • qwen3.5-plus(多模态)
        • qwen3.5-flash(多模态)
        • qwen3.5-122b-a10b(多模态)
        • qwen3.5-35b-a3b
        • qwen3.5-omni-plus(视频+文本)
        • qwen3.5-omni-plus(音频+文本)
        • qwen3.5-omni-plus(图片+文本)
        • qwen3.5-omni-flash(图片+文本)
        • qwen-deep-research
      • 百度
        • ernie-x1-32k
        • ernie-5.0-thinking-exp
        • ernie-5.0-thinking-preview
      • 腾讯
        • hunyuan-t1-latest
      • 讯飞
        • SparkDesk-v4.0
      • 蚂蚁
        • Ling-1T
        • Ring-1T
      • 阶跃星辰
        • step-3
      • 小米
        • mimo-v2-pro
        • mimo-v2-omni(多模态理解)
    • 图片生成
      • 通用接口
        • 图片生成
        • 图片编辑
      • OpenAI
        • gpt-image系列图片生成
        • gpt-image系列图片编辑
        • dall-e-3图片生成
      • Google
        • gemini-3-pro-image-preview
        • gemini-3.1-flash-image-preview
        • gemini-2.5-flash-image
        • gemini-2.5-flash-image-preview
      • Anthropic
      • BFL
        • flux-1-kontext-pro
      • ImagineArt
        • imagineart-1.5-preview
      • 字节跳动豆包
        • doubao-seedream-5-0-260128(文生图)
        • doubao-seedream-5-0-260128(图生图)
      • 阿里巴巴
        • wan2.7-image-文生图
        • wan2.7-image-图生图
    • 音视频处理
      • OpenAI
        • gpt-4o-audio-preview
        • gpt-audio
      • 阿里巴巴
        • qwen3-tts-flash
      • Elevenlabs
        • eleven_v3
      • MiniMax
        • 通用接口
    • 视频生成
      • Vidu
        • vidu-2.0-img2video
        • viduq2-pro提交图生视频任务
        • viduq3-pro图生视频
        • viduq3-pro文生视频
      • 字节跳动
        • 提交图生视频生成任务
        • 提交文生视频生成任务
      • 快手
        • kling提交图生视频任务
        • kling提交文生视频任务
        • kling-v3提交图生视频任务
        • kling-v3提交文生视频任务
      • 腾讯
        • hunyuan-video-v1.5
      • 海螺
        • 提交图生视频任务
        • 提交文生视频任务
      • sora
        • sora-2
        • sora-2-pro
      • Google
        • veo-3.1-generate-preview
        • veo-3.1-fast-generate-preview
      • 阿里巴巴
        • wan2.6-i2v
        • wan2.6-t2v
        • wan2.7-i2v提交图生视频任务
        • wan2.7-t2v提交文生视频任务
      • pixverse
        • pixverse-v5.5-t2v
        • pixverse-v5.5-i2v
        • pixverse-v5.6-i2v
        • pixverse-v5.6-t2v
      • ltx
        • 文生视频
        • 图生视频
      • worldlabs
        • 文生视频(world)
        • 图生视频(world)
        • 多图生视频(world)
      • runawayml
        • 提交gen4.5(文生视频)
        • 提交gen4.5(图生视频)
    • 多模态模型
      • 智谱
        • glm-4.5v
        • glm-4.6v
      • 阿里巴巴
        • qwen2.5-omni-7b
        • qwen2.5-vl-72b-instruct
        • qwen3-omni-flash-2025-12-01
      • 日日新
        • SenseNova-V6-Pro
    • Realtime
      • OpenAI
        • gpt-4o-mini-realtime-preview
        • gpt-4o-realtime-preview
        • gpt-realtime-1.5
      • qwen阿里云
        • qwen3-omni-flash-realtime
      • gemini
        • gemini-3.1-flash-live-preview
    • 向量化
      • OpenAI
        • text-embedding-3-large
        • text-embedding-3-small
        • text-embedding-ada-002
        • text-embedding-v1
    • 代码解释器
      • qwen3-模型
  • 已支持的搜索服务
    • serper
      • get方式
      • post方式
  • gemini
    • openai格式
      • gpt-5.2
      • openai格式
      • gemini-开启thinking
    • google原生格式
      • gemini-robotics-er-1.5-preview
      • google格式
  • anthropic
    • anthropic格式
      • anthropic格式
      • tools调用
      • thinking
      • image理解
      • code_execution
      • 关闭thinking
    • openai格式
      • openai格式
  • openai
    • chat/completions
    • 流式/chat/completions
    • responses
  • deepseek
    • chat/completions
  • qwen
    • qwen提交文生视频任务
    • qwen提交图生视频任务
    • qwen3-omni-flash
    • chat/completions(开启thinking)
    • chat/completions
  • 获取异步任务结果
    GET
  1. OpenAI

gpt-5.2(responses)

POST
/v1/responses

Input:text,image#

Output:text#

Request

Authorization
Provide your bearer token in the
Authorization
header when making requests to protected resources.
Example:
Authorization: Bearer ********************
Body Params application/jsonRequired

Example
{
    "model": "gpt-5.2",
    "input": "How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
    "reasoning": {
        "effort": "medium"
    }
}

Request Code Samples

Shell
JavaScript
Java
Swift
Go
PHP
Python
HTTP
C
C#
Objective-C
Ruby
OCaml
Dart
R
Request Request Example
Shell
JavaScript
Java
Swift
curl --location --request POST 'https://api.toiotech.com/v1/responses' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
    "model": "gpt-5.2",
    "input": "How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
    "reasoning": {
        "effort": "medium"
    }
}'

Responses

🟢200OK
application/json
Body

Example
{
  "id": "resp_0410d6669aa8711c00699fc1a696648190ab50c75e5a25f317",
  "object": "response",
  "model": "gpt-5.2",
  "usage": {
    "total_tokens": 484,
    "input_tokens": 25,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 459,
    "output_tokens_details": {
      "reasoning_tokens": 272
    }
  },
  "created_at": 1772077478,
  "status": "completed",
  "background": false,
  "completed_at": 1772077484,
  "content_filters": [
    {
      "blocked": false,
      "source_type": "prompt",
      "content_filter_raw": [],
      "content_filter_results": {
        "jailbreak": {
          "detected": false,
          "filtered": false
        },
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        }
      },
      "content_filter_offsets": {
        "start_offset": 0,
        "end_offset": 106,
        "check_offset": 0
      }
    },
    {
      "blocked": false,
      "source_type": "completion",
      "content_filter_raw": [],
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "protected_material_code": {
          "detected": false,
          "filtered": false
        },
        "protected_material_text": {
          "detected": false,
          "filtered": false
        }
      },
      "content_filter_offsets": {
        "start_offset": 0,
        "end_offset": 1305,
        "check_offset": 0
      }
    }
  ],
  "error": null,
  "frequency_penalty": 0.0,
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "output": [
    {
      "id": "rs_0410d6669aa8711c00699fc1a6e0688190bd8436c1b914b8b2",
      "type": "reasoning",
      "summary": []
    },
    {
      "id": "msg_0410d6669aa8711c00699fc1aadc048190b94721ca470ab3b7",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "Using a commonly cited exterior surface area for the Statue of Liberty’s copper skin of about **2,880 m²** (≈ **31,000 ft²**), a **1 mm** thick gold layer would require:\n\n- **Volume of gold** = area × thickness  \n  = 2,880 m² × 0.001 m = **2.88 m³**\n\n- **Mass of gold** = volume × density of gold (19,320 kg/m³)  \n  = 2.88 m³ × 19,320 kg/m³ ≈ **55,600 kg**\n\nSo, you’d need about **5.6×10⁴ kg of gold**, i.e. **~56 metric tons** (≈ **123,000 lb**, or **~1.79 million troy ounces**), assuming an even 1 mm coat and no losses."
        }
      ],
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0.0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "prompt_cache_retention": null,
  "reasoning": {
    "effort": "medium",
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "store": true,
  "temperature": 1.0,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.98,
  "truncation": "disabled",
  "user": null,
  "metadata": {}
}
Modified at 2026-02-26 03:45:10
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