通过调用模型效果盲测接口，开发者可以实现一次调用过程中，使用多种不同的模型进行内容生成。适用于在实际业务场景下，做用户对不同模型底座的智能体的喜好对比。
说明：
通过本接口进行模型效果测评后，您可以使用效果反馈接口，将测评数据反馈给百宝箱，百宝箱会将收集到的反馈数据进行计分排行，用于展示在不同业务场景下，最适配的底层模型。
前提条件
在调用本接口前，请先完成应用的发布，若无，请参见：发布应用，完成相关事项。
请求地址
百宝箱支持开发者在以下两个请求地址中选择任意一个地址发起请求。
- 地址 1：
POSThttps://api.tbox.cn/api/model/responses - 地址 2：
POSThttps://api.tbox.cn/api/model/chat/completions
请求头
请求参数
下文将按照请求地址的不同分别介绍请求参数。
model/responses
参数名 |
是否必填 |
类型 |
说明 |
示例 |
model |
是 |
String |
需要测试的模型。支持：
|
auto |
input |
是 |
String |
替换为您想要提问的问题。如果是多轮对话，需要符合 OpenAI messages 协议。 |
今天的国际金价是多少 |
user |
是 |
String |
用户 ID。 |
- |
tools |
否 |
Array |
模型效果盲测中内置的全网搜索工具。 |
|
extra_body |
否 |
Object |
目前只接收一个参数n，本次需要使用几个模型进行生成，仅当model=auto 时生效。 |
"n": 3 |
请求示例
curl --location 'https://api.tbox.cn/api/model/responses' \ --header 'Authorization: Bearer {your_token}' \ --header 'AppCode: {app_code}' \ --header 'Content-Type: application/json' \ --header 'Accept: text/event-stream' \ --header 'Cookie: receive-cookie-deprecation=1' \ --data '{ "model": "auto", "input": "今天的国际金价是多少？", "user": "{user_id}", "tools": [ { "type": "web_search_preview" } ], "stream": true, "extra_body": { "n": 3 } }'
model/completions
参数名 |
是否必填 |
类型 |
说明 |
示例 |
model |
是 |
String |
需要测试的模型。支持：
|
auto |
messages |
是 |
Object |
替换为您想要提问的问题。如果是多轮对话，需要符合 OpenAI messages 协议。 |
|
n |
是 |
String |
本次测评涉及的模型数量。
|
3 |
stream |
是 |
Boolean |
是否使用流式输出响应内容。 |
false |
请求示例
curl --location 'https://api.tbox.cn/api/model/chat/completions' \ --header 'Authorization: Bearer {your_token}' \ --header 'AppCode: {app_code}' \ --header 'Content-Type: application/json' \ --data '{ "model": "auto", "messages": [ {"role": "system", "content": "你是一个专业翻译，保持原文风格"}, {"role": "user", "content": "The pursuit of knowledge is a lifelong journey"} ], "n": 2, "stream": false }'
返回参数
model/responses
参数 |
类型 |
说明 |
示例 |
event |
String |
事件类型 |
response.output_text.delta |
data.type |
String |
事件详细类型 |
response.output_text.delta |
data.response.id |
String |
响应唯一 ID |
resp_7b151ed6e2fc4cb899ff433886b81664 |
data.response.status |
String |
当前状态 |
in_progress / completed |
data.response.model |
String |
使用的模型名称 |
qwen-max-latest |
data.item_id |
String |
输出项 ID |
7c83dc6375b443cbb3b1e27c0dd3ff75 |
data.output_index |
Integer |
输出项索引位置 |
0 |
data.content_index |
Integer |
内容段索引位置 |
0 |
data.delta |
String |
增量文本内容（Unicode 编码） |
\u4eca\u5929 |
data.tool_id |
String |
工具调用唯一 ID |
call_IyGKdnpKpJ6m1maVLA9kdksE |
返回示例
event: response.created data: {"type": "response.created", "response": {"id": "resp_7b151ed6e2fc4cb899ff433886b81664", "object": "response", "created_at": 1749721513, "status": "in_progress", "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": 512, "model": "qwen-max-latest", "output": [], "parallel_tool_calls": false, "previous_response_id": "", "reasoning": {"effort": 0, "summary": null}, "store": true, "temperature": 0.7, "text": {"format": {"type": "text"}}, "tool_choice": "auto", "tools": [{"type": "web_search_preview"}], "top_p": 0.9, "truncation": "disabled", "usage": null, "user": "ezK16lM", "metadata": {}}} event: response.in_progress data: {"type": "response.in_progress", "response": {"id": "resp_7b151ed6e2fc4cb899ff433886b81664", "object": "response", "created_at": 1749721513, "status": "in_progress", "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": 512, "model": "qwen-max-latest", "output": [], "parallel_tool_calls": false, "previous_response_id": "", "reasoning": {"effort": 0, "summary": null}, "store": true, "temperature": 0.7, "text": {"format": {"type": "text"}}, "tool_choice": "auto", "tools": [{"type": "web_search_preview"}], "top_p": 0.9, "truncation": "disabled", "usage": null, "user": "ezK16lM", "metadata": {}}} event: response.output_item.added data: {"type": "response.output_item.added", "item": {"id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "status": "in_progress", "type": "message", "role": "assistant", "content": []}, "output_index": 0} event: response.web_search_call.in_progress data: {"type": "response.web_search_call.in_progress", "output_index": 0, "model": "qwen-max-latest", "item_id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "tool_id": "call_IyGKdnpKpJ6m1maVLA9kdksE", "tool_input": ""} ... event: response.output_text.delta data: {"type": "response.output_text.delta", "model": "qwen-max-latest", "item_id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "output_index": 18, "content_index": 0, "delta": "\u4eca\u5929"} event: response.output_text.delta data: {"type": "response.output_text.delta", "model": "qwen-max-latest", "item_id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "output_index": 19, "content_index": 0, "delta": "\u7684"} ... event: response.output_item.done data: {"type": "response.output_item.done", "output_index": 98, "item": {"id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "type": "message", "status": "completed", "role": "assistant", "content": [{"type": "output_text", "text": "\u6211\u53ef\u4ee5\u5e2e\u60a8\u67e5\u8be2\u4eca\u5929\u7684\u56fd\u9645\u91d1\u4ef7\u3002\u8ba9\u6211\u901a\u8fc7\u641c\u7d22\u83b7\u53d6\u6700\u65b0\u4fe1\u606f\u3002\u6839\u636e\u6700\u65b0\u641c\u7d22\u7ed3\u679c\uff0c\u4eca\u5929\u7684\u56fd\u9645\u91d1\u4ef7\u4fe1\u606f\u5982\u4e0b\uff1a\n\n**\u6700\u65b0\u56fd\u9645\u91d1\u4ef7\uff082025\u5e743\u670826\u65e5\uff09\uff1a**\n- \u56fd\u9645\u9ec4\u91d1\u4ef7\u683c\uff1a3017.77 \u7f8e\u5143/\u76ce\u53f8\n\n\u6b64\u5916\uff0c\u6839\u636e\u5176\u4ed6\u641c\u7d22\u7ed3\u679c\u663e\u793a\u7684\u6700\u8fd1\u6570\u636e\uff1a\n- \u4f26\u6566\u73b0\u8d27\u9ec4\u91d1\u4ef7\u683c\uff1a2936.57 \u7f8e\u5143/\u76ce\u53f8\uff08\u6da8\u5e45+0.049%\uff09\n- \u7ebd\u7ea6\u671f\u8d27\u56fd\u9645\u91d1\u4ef7\uff1a2953.24 \u7f8e\u5143/\u76ce\u53f8\uff08\u6da8\u5e45+0.144%\uff09\n- \u4e2d\u56fd\u9ec4\u91d1\u4ef7\u683c\uff08\u4e0a\u6d77\u9ec4\u91d1\u4ea4\u6613\u6240\uff09\uff1a683.73 \u4eba\u6c11\u5e01/\u514b\uff08\u8dcc\u5e45-0.401%\uff09\n\n\u8bf7\u6ce8\u610f\uff0c\u91d1\u4ef7\u4f1a\u968f\u5e02\u573a\u6ce2\u52a8\u800c\u53d8\u5316\uff0c\u4ee5\u4e0a\u6570\u636e\u4ec5\u4f9b\u53c2\u8003\uff0c\u5b9e\u9645\u4ea4\u6613\u8bf7\u4ee5\u5b98\u65b9\u62a5\u4ef7\u4e3a\u51c6\u3002", "annotations": []}], "model": "qwen-max-latest"}} event: response.completed data: {"id": "resp_7b151ed6e2fc4cb899ff433886b81664", "object": "response", "created_at": 1749721513, "status": "completed", "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": 512, "model": "qwen-max-latest", "output": [{"id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "type": "message", "status": "completed", "role": "assistant", "content": [{"type": "output_text", "text": "\u4eca\u5929\u7684\u56fd\u9645\u91d1\u4ef7\u4e3a2861.81\u7f8e\u5143/\u76ce\u53f8\uff0c\u6298\u5408\u4eba\u6c11\u5e01\u4e3a670.70\u5143/\u514b\u3002\u8bf7\u6ce8\u610f\uff0c\u5177\u4f53\u4ef7\u683c\u53ef\u80fd\u4f1a\u56e0\u5e02\u573a\u6ce2\u52a8\u800c\u6709\u6240\u53d8\u5316\u3002", "annotations": []}], "model": "qwen-max-latest"}, {"id": "7c83dc6375b443cbb3b1e27c0dd3ff75", "type": "message", "status": "completed", "role": "assistant", "content": [{"type": "output_text", "text": "\u6211\u53ef\u4ee5\u5e2e\u60a8\u67e5\u8be2\u4eca\u5929\u7684\u56fd\u9645\u91d1\u4ef7\u3002\u8ba9\u6211\u901a\u8fc7\u641c\u7d22\u83b7\u53d6\u6700\u65b0\u4fe1\u606f\u3002\u6839\u636e\u6700\u65b0\u641c\u7d22\u7ed3\u679c\uff0c\u4eca\u5929\u7684\u56fd\u9645\u91d1\u4ef7\u4fe1\u606f\u5982\u4e0b\uff1a\n\n**\u6700\u65b0\u56fd\u9645\u91d1\u4ef7\uff082025\u5e743\u670826\u65e5\uff09\uff1a**\n- \u56fd\u9645\u9ec4\u91d1\u4ef7\u683c\uff1a3017.77 \u7f8e\u5143/\u76ce\u53f8\n\n\u6b64\u5916\uff0c\u6839\u636e\u5176\u4ed6\u641c\u7d22\u7ed3\u679c\u663e\u793a\u7684\u6700\u8fd1\u6570\u636e\uff1a\n- \u4f26\u6566\u73b0\u8d27\u9ec4\u91d1\u4ef7\u683c\uff1a2936.57 \u7f8e\u5143/\u76ce\u53f8\uff08\u6da8\u5e45+0.049%\uff09\n- \u7ebd\u7ea6\u671f\u8d27\u56fd\u9645\u91d1\u4ef7\uff1a2953.24 \u7f8e\u5143/\u76ce\u53f8\uff08\u6da8\u5e45+0.144%\uff09\n- \u4e2d\u56fd\u9ec4\u91d1\u4ef7\u683c\uff08\u4e0a\u6d77\u9ec4\u91d1\u4ea4\u6613\u6240\uff09\uff1a683.73 \u4eba\u6c11\u5e01/\u514b\uff08\u8dcc\u5e45-0.401%\uff09\n\n\u8bf7\u6ce8\u610f\uff0c\u91d1\u4ef7\u4f1a\u968f\u5e02\u573a\u6ce2\u52a8\u800c\u53d8\u5316\uff0c\u4ee5\u4e0a\u6570\u636e\u4ec5\u4f9b\u53c2\u8003\uff0c\u5b9e\u9645\u4ea4\u6613\u8bf7\u4ee5\u5b98\u65b9\u62a5\u4ef7\u4e3a\u51c6\u3002", "annotations": []}], "model": "anthropic/claude-3.7-sonnet"}], "parallel_tool_calls": false, "previous_response_id": "", "reasoning": {"effort": 0, "summary": null}, "store": true, "temperature": 0.7, "text": {"format": {"type": "text"}}, "tool_choice": "auto", "tools": [{"type": "web_search_preview"}], "top_p": 0.9, "truncation": "disabled", "usage": null, "user": "ezK16lM", "metadata": {}, "type": "response.completed"} event: response.completed data: [DONE]
model/completions
参数 |
类型 |
说明 |
id |
String |
本次请求的唯一标识 |
object |
String |
固定值 |
created |
Integer |
响应生成的时间戳（Unix 时间戳，单位：秒） |
models |
Array[Str] |
使用的模型列表 |
choices |
Array[Choice] |
包含模型生成的文本结果数组，数组长度通常和 n 值相同； |
usage |
Usage |
Token 消耗统计信息 |
choice
参数 |
类型 |
说明 |
finish_reason |
String |
生成结束原因（示例： |
index |
Integer |
结果在 |
logprobs |
null |
保留字段 |
message |
ChoiceMessage |
模型生成的消息内容 |
content_filter_results |
Object |
内容安全过滤结果 |
message_id |
String |
消息的唯一标识 |
model |
String |
生成此结果的模型名称 |
ChoiceMessage
字段名 |
类型 |
说明 |
content |
String |
核心输出，模型生成的文本内容； |
refusal |
null |
拒绝回答时的说明（ |
role |
String |
消息角色（固定为 |
audio |
null |
音频输出（ |
function_call |
null |
函数调用请求（ |
tool_calls |
null |
工具调用请求（ |
Usage
字段名 |
类型 |
说明 |
prompt_tokens |
Integer |
输入提示消耗的 Token 数量 |
completion_tokens |
Integer |
输出结果消耗的 Token 数量 |
total_tokens |
Integer |
总 Token 消耗量 |
返回示例
{ "id": "9f10f75262614014b495357c15c8a694", "object": "chat.completion", "created": 1750411923, "models": [ "meta-llama/llama-4-scout-17b-16e-instruct", "deepseek/deepseek-r1-distill-qwen-14b" ], "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "求知是终身的旅程", "refusal": null, "role": "assistant", "audio": null, "function_call": null, "tool_calls": null }, "content_filter_results": { "hate": { "filtered": false }, "self_harm": { "filtered": false }, "sexual": { "filtered": false }, "violence": { "filtered": false }, "jailbreak": { "filtered": false, "detected": false }, "profanity": { "filtered": false, "detected": false } }, "message_id": "bae9679e151c40e980605257e9eb162e", "model": "meta-llama/llama-4-scout-17b-16e-instruct" }, { "finish_reason": "stop", "index": 1, "logprobs": null, "message": { "content": "<think>\nAlright, so I need to translate the sentence \"The pursuit of knowledge is a lifelong journey\" into Chinese. Let me think about how to approach this.\n\nFirst, the main idea is \"the pursuit of knowledge.\" In Chinese, that would probably be \"知识的追求.\" But wait, sometimes people use \"知识追求\" without the 的, but maybe adding 的 makes it smoother. I think \"知识的追求\" sounds better here.\n\nNext, the verb \"is\" translates to \"是.\" Then \"a lifelong journey.\" \"Lifelong\" can be translated as \"一生的\" or \"毕生的.\" I think \"毕生的\" might sound a bit more elegant, like \"毕生的旅程.\" Alternatively, \"一生的旅程\" is also correct, but \"毕生的旅程\" feels more poetic, which suits the original sentence's tone.\n\nPutting it all together: \"知识的追求是一生的旅程\" or \"知识的追求是毕生的旅程.\" I think between these two, \"毕生的旅程\" adds a touch of formality and depth, matching the idea of a lifelong pursuit.\n\nAnother option is \"知识的追求是一生的旅程,\" which is straightforward and clear. It depends on the context whether to go for elegance or simplicity. Since the original sentence has a philosophical tone, maybe leaning towards the more elegant version is better.\n\nWait, I can also consider the flow. \"知识的追求\" as a noun phrase feels a bit heavy, so maybe \"追求知识\" could work too, but then it changes the structure. But the original sentence starts with \"The pursuit of knowledge,\" which is a noun phrase, so to keep that structure, \"知识的追求\" is better.\n\nSo, finalizing, \"知识的追求是一生的旅程\" or \"知识的追求是毕生的旅程.\" Either is fine, but I think the second one has a nicer ring to it.\n</think>\n\n知识的追求是毕生的旅程。", "refusal": null, "role": "assistant", "audio": null, "function_call": null, "tool_calls": null }, "content_filter_results": { "hate": { "filtered": false }, "self_harm": { "filtered": false }, "sexual": { "filtered": false }, "violence": { "filtered": false }, "jailbreak": { "filtered": false, "detected": false }, "profanity": { "filtered": false, "detected": false } }, "message_id": "348903ddedf943b7a12185843243c1d6", "model": "deepseek/deepseek-r1-distill-qwen-14b" } ], "usage": { "prompt_tokens": 49, "completion_tokens": 417, "total_tokens": 466 } }
后续可选操作
通过本文档所述接口进行模型效果测评后，您可以使用效果反馈接口，将测评数据反馈给百宝箱，百宝箱将收集到的反馈数据进行计分排行，用于展示在不同业务场景下，最适配的底层模型。接口说明请参见：盲测结果反馈。
常见问题
使用模型效果盲测（含结果）能力时，开发者可能遇见的问题以及对应的解法可参见：常见问题。
