大模型服务平台 TokenHub Xiaomi MiMo 调用指南

一、概述

注意：TokenHub 当前上线的所有 MiMo 模型，该模型默认开启深度思考。若在多轮工具调用场景中开启思考模式，建议完整回传历史 reasoning_content 以保持最佳效果；TokenHub 侧缺失该字段不会报错，但可能影响模型表现，详见这篇 多轮工具调用回传 reasoning_content。

二、支持的模型

TokenHub 当前支持以下 MiMo 模型（具体以 模型列表 为准）：

模型主要能力：深度思考、Function Calling、结构化输出、Cache 缓存、流式输出。

MiMo 模型对三种协议的支持情况请参见 语言模型调用概览。

三、前提条件

·已注册腾讯云账号并开通 TokenHub 服务。

·已在 TokenHub 控制台 获取 API Key。

·已根据所用语言安装对应 SDK 或具备 HTTP 请求能力。

四、协议与接入地址

Base URL（按接入地域二选一）

·广州：https://tokenhub.tencentmaas.com/v1

·新加坡：https://tokenhub-intl.tencentmaas.com/v1

协议路径与认证方式

注意：说明：

注意：这篇示例默认使用广州地域与 OpenAI Chat Completions 协议，新加坡地域只需替换 Base URL。Responses 与 Anthropic 协议的调用方式参见这篇 Anthropic Messages 协议调用、Responses API 协议调用，完整字段说明请参见 OpenAI Chat Completions 协议字段说明、OpenAI Response 协议字段说明、Anthropic Message 协议字段说明。

五、快速开始

以下示例展示最简单的单轮对话调用，请将 YOUR_API_KEY 替换为你创建的 API Key。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "你好，请介绍一下你自己"} ], "max_tokens": 2048 }'# pip install openaifrom openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="mimo-v2.5-pro", messages=[ {"role": "user", "content": "你好，请介绍一下你自己"} ], max_tokens=2048,)print(response.choices[0].message.content)// npm install openaiimport OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const response = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [ { role: "user", content: "你好，请介绍一下你自己" } ], max_tokens: 2048,});console.log(response.choices[0].message.content);// 使用 OkHttp，添加依赖：implementation("com.squareup.okhttp3:okhttp:4.12.0")import okhttp3.*;import org.json.*;OkHttpClient httpClient = new OkHttpClient();JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 2048);JSONArray messages = new JSONArray();messages.put(new JSONObject().put("role", "user").put("content", "你好，请介绍一下你自己"));body.put("messages", messages);Request request = new Request.Builder() .url("https://tokenhub.tencentmaas.com/v1/chat/completions") .addHeader("Authorization", "Bearer YOUR_API_KEY") .addHeader("Content-Type", "application/json") .post(RequestBody.create(body.toString(), MediaType.get("application/json"))) .build();try (Response response = httpClient.newCall(request).execute()) { JSONObject result = new JSONObject(response.body().string()); System.out.println(result.getJSONArray("choices") .getJSONObject(0).getJSONObject("message").getString("content"));}package mainimport ( "bytes" "encoding/json" "fmt" "io" "net/http")func main() { body := map[string]interface{}{ "model": "mimo-v2.5-pro", "messages": []map[string]string{ {"role": "user", "content": "你好，请介绍一下你自己"}, }, "max_tokens": 2048, } data, _ := json.Marshal(body) req, _ := http.NewRequest("POST", "https://tokenhub.tencentmaas.com/v1/chat/completions", bytes.NewBuffer(data)) req.Header.Set("Authorization", "Bearer YOUR_API_KEY") req.Header.Set("Content-Type", "application/json") resp, _ := http.DefaultClient.Do(req) defer resp.Body.Close() respBody, _ := io.ReadAll(resp.Body) var result map[string]interface{} json.Unmarshal(respBody, &result) choices := result["choices"].([]interface{}) msg := choices[0].(map[string]interface{})["message"].(map[string]interface{}) fmt.Println(msg["content"])}

七、基础对话

发送单轮对话请求，获取模型回复。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "介绍一下大语言模型"} ], "max_tokens": 2048 }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="mimo-v2.5-pro", messages=[ {"role": "user", "content": "介绍一下大语言模型"} ], max_tokens=2048,)print(response.choices[0].message.content)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const response = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [ { role: "user", content: "介绍一下大语言模型" } ], max_tokens: 2048,});console.log(response.choices[0].message.content);import okhttp3.*;import org.json.*;OkHttpClient httpClient = new OkHttpClient();JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 2048);body.put("messages", new JSONArray() .put(new JSONObject().put("role", "user").put("content", "介绍一下大语言模型")));Request request = new Request.Builder() .url("https://tokenhub.tencentmaas.com/v1/chat/completions") .addHeader("Authorization", "Bearer YOUR_API_KEY") .addHeader("Content-Type", "application/json") .post(RequestBody.create(body.toString(), MediaType.get("application/json"))) .build();try (Response response = httpClient.newCall(request).execute()) { JSONObject result = new JSONObject(response.body().string()); System.out.println(result.getJSONArray("choices") .getJSONObject(0).getJSONObject("message").getString("content"));}body := map[string]interface{}{ "model": "mimo-v2.5-pro", "messages": []map[string]string{ {"role": "user", "content": "介绍一下大语言模型"}, }, "max_tokens": 2048,}// … 其余请求代码同快速开始示例

八、流式输出

将 stream 设置为 true 开启 SSE 流式输出。MiMo-V2.5-Pro 默认开启深度思考，响应耗时相对较长，建议长文本或复杂推理场景统一开启流式输出，避免请求超时。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "写一首关于春天的短诗"} ], "max_tokens": 2048, "stream": true }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)stream = client.chat.completions.create( model="mimo-v2.5-pro", messages=[ {"role": "user", "content": "写一首关于春天的短诗"} ], max_tokens=2048, stream=True,)for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const stream = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [ { role: "user", content: "写一首关于春天的短诗" } ], max_tokens: 2048, stream: true,});for await (const chunk of stream) { const content = chunk.choices[0]?.delta?.content; if (content) process.stdout.write(content);}import okhttp3.*;import okhttp3.sse.*;import org.json.*;OkHttpClient httpClient = new OkHttpClient();JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 2048);body.put("stream", true);body.put("messages", new JSONArray() .put(new JSONObject().put("role", "user").put("content", "写一首关于春天的短诗")));Request request = new Request.Builder() .url("https://tokenhub.tencentmaas.com/v1/chat/completions") .addHeader("Authorization", "Bearer YOUR_API_KEY") .addHeader("Content-Type", "application/json") .post(RequestBody.create(body.toString(), MediaType.get("application/json"))) .build();EventSources.createFactory(httpClient).newEventSource(request, new EventSourceListener() { @Override public void onEvent(EventSource source, String id, String type, String data) { if ("[DONE]".equals(data)) return; try { JSONObject json = new JSONObject(data); JSONObject delta = json.getJSONArray("choices").getJSONObject(0).getJSONObject("delta"); String content = delta.optString("content", ""); if (!content.isEmpty()) System.out.print(content); } catch (JSONException ignored) {} }});import ( "bufio" "bytes" "encoding/json" "fmt" "net/http" "strings")body := map[string]interface{}{ "model": "mimo-v2.5-pro", "messages": []map[string]string{{"role": "user", "content": "写一首关于春天的短诗"}}, "max_tokens": 2048, "stream": true,}data, _ := json.Marshal(body)req, _ := http.NewRequest("POST", "https://tokenhub.tencentmaas.com/v1/chat/completions", bytes.NewBuffer(data))req.Header.Set("Authorization", "Bearer YOUR_API_KEY")req.Header.Set("Content-Type", "application/json")resp, _ := http.DefaultClient.Do(req)defer resp.Body.Close()scanner := bufio.NewScanner(resp.Body)for scanner.Scan() { line := scanner.Text() if !strings.HasPrefix(line, "data: ") || line == "data: [DONE]" { continue } var chunk map[string]interface{} json.Unmarshal([]byte(strings.TrimPrefix(line, "data: ")), &chunk) choices := chunk["choices"].([]interface{}) delta := choices[0].(map[string]interface{})["delta"].(map[string]interface{}) if content, ok := delta["content"].(string); ok { fmt.Print(content) }}

九、System Prompt

通过 system 角色消息设置模型的行为指令和背景信息。

注意：说明：

注意：建议在 system 消息中注入当前日期（可同时携带身份与角色说明），可提升时间类问题的回答准确度。通用模板如下（{date} 与 {week} 替换为实际日期与星期）：

注意：你是一位乐于助人的 AI 智能助手。今天的日期：{date} {week}。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "system", "content": "你是一位专业的 Python 编程助手，只回答与 Python 相关的问题，回答简洁明了。"}, {"role": "user", "content": "如何读取一个 CSV 文件？"} ], "max_tokens": 2048 }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="mimo-v2.5-pro", messages=[ { "role": "system", "content": "你是一位专业的 Python 编程助手，只回答与 Python 相关的问题，回答简洁明了。", }, {"role": "user", "content": "如何读取一个 CSV 文件？"}, ], max_tokens=2048,)print(response.choices[0].message.content)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const response = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [ { role: "system", content: "你是一位专业的 Python 编程助手，只回答与 Python 相关的问题，回答简洁明了。", }, { role: "user", content: "如何读取一个 CSV 文件？" }, ], max_tokens: 2048,});console.log(response.choices[0].message.content);JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 2048);body.put("messages", new JSONArray() .put(new JSONObject().put("role", "system") .put("content", "你是一位专业的 Python 编程助手，只回答与 Python 相关的问题，回答简洁明了。")) .put(new JSONObject().put("role", "user") .put("content", "如何读取一个 CSV 文件？")));// … 发送请求代码同上body := map[string]interface{}{ "model": "mimo-v2.5-pro", "messages": []map[string]string{ {"role": "system", "content": "你是一位专业的 Python 编程助手，只回答与 Python 相关的问题，回答简洁明了。"}, {"role": "user", "content": "如何读取一个 CSV 文件？"}, }, "max_tokens": 2048,}// … 发送请求代码同快速开始

十、多轮对话

将历史消息一并传入 messages 数组，即可实现上下文记忆的多轮对话。

注意：说明：

注意：纯对话场景（历史消息中不含工具调用）只需回写 content，无需回写 reasoning_content，可有效减少 token 消耗；一旦历史消息中出现过工具调用，则建议完整回传 reasoning_content 以保持最佳效果，详见这篇 多轮工具调用回传 reasoning_content。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "我叫小明，我喜欢打篮球"}, {"role": "assistant", "content": "你好，小明！打篮球是一项很棒的运动。"}, {"role": "user", "content": "你还记得我的名字和爱好吗？"} ], "max_tokens": 2048 }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)# 维护对话历史conversation = [ {"role": "system", "content": "你是一个友好的 AI 助手。"},]def chat(user_input): conversation.append({"role": "user", "content": user_input}) response = client.chat.completions.create( model="mimo-v2.5-pro", messages=conversation, max_tokens=2048, ) reply = response.choices[0].message.content # 纯对话场景只回写 content，不回写 reasoning_content conversation.append({"role": "assistant", "content": reply}) return replyprint(chat("我叫小明，我喜欢打篮球"))print(chat("你还记得我的名字和爱好吗？"))import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const conversation = [ { role: "system", content: "你是一个友好的 AI 助手。" },];async function chat(userInput) { conversation.push({ role: "user", content: userInput }); const response = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: conversation, max_tokens: 2048, }); const reply = response.choices[0].message.content; conversation.push({ role: "assistant", content: reply }); return reply;}console.log(await chat("我叫小明，我喜欢打篮球"));console.log(await chat("你还记得我的名字和爱好吗？"));JSONArray messages = new JSONArray();messages.put(new JSONObject().put("role", "system").put("content", "你是一个友好的 AI 助手。"));messages.put(new JSONObject().put("role", "user").put("content", "我叫小明，我喜欢打篮球"));messages.put(new JSONObject().put("role", "assistant").put("content", "你好，小明！打篮球是一项很棒的运动。"));messages.put(new JSONObject().put("role", "user").put("content", "你还记得我的名字和爱好吗？"));JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("messages", messages);body.put("max_tokens", 2048);// … 发送请求代码同上body := map[string]interface{}{ "model": "mimo-v2.5-pro", "messages": []map[string]string{ {"role": "system", "content": "你是一个友好的 AI 助手。"}, {"role": "user", "content": "我叫小明，我喜欢打篮球"}, {"role": "assistant", "content": "你好，小明！打篮球是一项很棒的运动。"}, {"role": "user", "content": "你还记得我的名字和爱好吗？"}, }, "max_tokens": 2048,}// … 发送请求代码同快速开始

十一、Function Calling（工具调用）

Function Calling 允许模型调用外部工具获取实时数据。模型本身不执行函数，而是返回应调用的函数名和参数，由用户代码执行后将结果传回模型，最终得到自然语言回答。

调用流程：

1. 用户提问 → 模型返回 tool_calls（包含函数名和参数）。

2. 用户代码执行该函数 → 将结果以 role: tool 消息传回。

3. 模型根据函数结果生成最终自然语言回答。

cURLPythonNode.jsJavaGo# 第一轮：发送问题 + 工具定义curl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "北京今天天气怎么样？"} ], "tools": [{ "type": "function", "function": { "name": "get_weather", "description": "获取指定城市的天气信息", "parameters": { "type": "object", "properties": { "city": {"type": "string", "description": "城市名称，如北京"} }, "required": ["city"] } } }], "tool_choice": "auto" }'# 第二轮：将工具执行结果传回（tool_call_id、reasoning_content 替换为第一轮实际返回值）curl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "北京今天天气怎么样？"}, {"role": "assistant", "content": "", "reasoning_content": "用户询问北京天气，需要调用 get_weather 工具获取实时数据。", "tool_calls": [{"id": "call_xxx", "type": "function", "function": {"name": "get_weather", "arguments": "{\\"city\\": \\"北京\\"}"}}]}, {"role": "tool", "tool_call_id": "call_xxx", "content": "晴，气温28℃，湿度50%"} ], "tools": [{"type": "function", "function": {"name": "get_weather", "description": "获取指定城市的天气信息", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}}}] }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)# 定义工具tools = [ { "type": "function", "function": { "name": "get_weather", "description": "获取指定城市的天气信息", "parameters": { "type": "object", "properties": { "city": {"type": "string", "description": "城市名称，如北京"} }, "required": ["city"], }, }, }]# 第一轮：发送问题messages = [{"role": "user", "content": "北京今天天气怎么样？"}]response = client.chat.completions.create( model="mimo-v2.5-pro", messages=messages, tools=tools, max_tokens=2048,)assistant_message = response.choices[0].message# 模型发起工具调用if response.choices[0].finish_reason == "tool_calls": tool_call = assistant_message.tool_calls[0] print(f"模型调用工具：{tool_call.function.name}，参数：{tool_call.function.arguments}") # 执行工具（此处为模拟返回） tool_result = "晴，气温28℃，湿度50%" # 第二轮：完整回传 assistant 消息（含 reasoning_content）+ 工具结果 messages.append(assistant_message) messages.append({ "role": "tool", "tool_call_id": tool_call.id, "content": tool_result, }) final_response = client.chat.completions.create( model="mimo-v2.5-pro", messages=messages, tools=tools, max_tokens=2048, ) print(final_response.choices[0].message.content)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const tools = [ { type: "function", function: { name: "get_weather", description: "获取指定城市的天气信息", parameters: { type: "object", properties: { city: { type: "string", description: "城市名称，如北京" }, }, required: ["city"], }, }, },];// 第一轮const messages = [{ role: "user", content: "北京今天天气怎么样？" }];const response1 = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages, tools, max_tokens: 2048,});const assistantMsg = response1.choices[0].message;if (response1.choices[0].finish_reason === "tool_calls") { const toolCall = assistantMsg.tool_calls[0]; console.log(`工具调用：${toolCall.function.name}，参数：${toolCall.function.arguments}`); const toolResult = "晴，气温28℃，湿度50%"; // 原样回传 assistantMsg（含 reasoning_content），不要手动裁剪字段 messages.push(assistantMsg); messages.push({ role: "tool", tool_call_id: toolCall.id, content: toolResult }); const response2 = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages, tools, max_tokens: 2048, }); console.log(response2.choices[0].message.content);}JSONObject toolFunc = new JSONObject() .put("name", "get_weather") .put("description", "获取指定城市的天气信息") .put("parameters", new JSONObject() .put("type", "object") .put("properties", new JSONObject() .put("city", new JSONObject().put("type", "string").put("description", "城市名称"))) .put("required", new JSONArray().put("city")));JSONArray tools = new JSONArray() .put(new JSONObject().put("type", "function").put("function", toolFunc));JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("messages", new JSONArray() .put(new JSONObject().put("role", "user").put("content", "北京今天天气怎么样？")));body.put("tools", tools);// … 发送请求，解析 tool_calls 与 reasoning_content，执行工具，构造第二轮请求body := map[string]interface{}{ "model": "mimo-v2.5-pro", "messages": []map[string]string{ {"role": "user", "content": "北京今天天气怎么样？"}, }, "tools": []map[string]interface{}{{ "type": "function", "function": map[string]interface{}{ "name": "get_weather", "description": "获取指定城市的天气信息", "parameters": map[string]interface{}{ "type": "object", "properties": map[string]interface{}{ "city": map[string]string{"type": "string", "description": "城市名称"}, }, "required": []string{"city"}, }, }, }},}// … 发送请求，解析 tool_calls 与 reasoning_content，构造第二轮请求

十二、思考模式

MiMo 系列模型，内置深度思考能力且默认开启。开启思考后，模型先通过内部思维链逐步分析问题，再输出最终答案，推理过程通过独立的 reasoning_content 字段返回，不与 content 混排。

十三、thinking 参数说明

十四、开启或关闭思考

cURLPythonNode.jsJavaGo# 关闭思考：直接作答，适合简单问答、格式转换等低延迟场景curl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "用一句话解释什么是机器学习"} ], "max_tokens": 1024, "thinking": {"type": "disabled"} }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)# 开启思考（默认行为，此处显式声明）response = client.chat.completions.create( model="mimo-v2.5-pro", messages=[{"role": "user", "content": "解方程 x^2 – 5x + 6 = 0"}], max_tokens=4096, extra_body={"thinking": {"type": "enabled"}},)msg = response.choices[0].message# 获取推理过程（思考模式专属字段）reasoning = getattr(msg, "reasoning_content", None)if reasoning: print("=== 推理过程 ===") print(reasoning)print("=== 最终答案 ===")print(msg.content)# 关闭思考fast_response = client.chat.completions.create( model="mimo-v2.5-pro", messages=[{"role": "user", "content": "用一句话解释什么是机器学习"}], max_tokens=1024, extra_body={"thinking": {"type": "disabled"}},)print(fast_response.choices[0].message.content)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const response = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [{ role: "user", content: "解方程 x^2 – 5x + 6 = 0" }], max_tokens: 4096, // @ts-ignore – thinking 为扩展字段 thinking: { type: "enabled" },});const msg = response.choices[0].message;const reasoning = (msg as any).reasoning_content;if (reasoning) { console.log("=== 推理过程 ==="); console.log(reasoning);}console.log("=== 最终答案 ===");console.log(msg.content);JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 4096);body.put("thinking", new JSONObject().put("type", "enabled"));body.put("messages", new JSONArray() .put(new JSONObject().put("role", "user").put("content", "解方程 x^2 – 5x + 6 = 0")));// … 发送请求try (Response response = httpClient.newCall(request).execute()) { JSONObject result = new JSONObject(response.body().string()); JSONObject message = result.getJSONArray("choices") .getJSONObject(0).getJSONObject("message"); String reasoning = message.optString("reasoning_content", ""); String content = message.getString("content"); System.out.println("推理过程: " + reasoning); System.out.println("最终答案: " + content);}body := map[string]interface{}{ "model": "mimo-v2.5-pro", "max_tokens": 4096, "thinking": map[string]string{"type": "enabled"}, "messages": []map[string]string{ {"role": "user", "content": "解方程 x^2 – 5x + 6 = 0"}, },}// … 发送请求，从响应中解析 reasoning_content 和 content 字段

注意：OpenAI SDK 的类型定义中没有 reasoning_content 字段，直接用属性访问会报错，必须通过安全取值方式读取：

十五、响应结构说明

开启思考时，推理过程在 reasoning_content 中返回，最终答案在 content 中返回。思考消耗的 token 计入 usage.completion_tokens 总量；当前 usage.completion_tokens_details.reasoning_tokens 恒为 0，无法单独拆分思考 token 用量：

{ "id": "2b92b0964c9b4335bffad7c2f75cfe9e", "choices": [{ "index": 0, "message": { "role": "assistant", "reasoning_content": "这是一个一元二次方程，先尝试因式分解：(x-2)(x-3) = 0，所以 x = 2 或 x = 3。", "content": "方程 x² – 5x + 6 = 0 的解为：**x = 2** 或 **x = 3**", "tool_calls": null }, "finish_reason": "stop" }], "model": "mimo-v2.5-pro", "object": "chat.completion", "usage": { "prompt_tokens": 25, "completion_tokens": 120, "total_tokens": 145, "completion_tokens_details": { "reasoning_tokens": 0 }, "prompt_tokens_details": { "cached_tokens": 0 } }}

关闭思考时，reasoning_content 不返回。

十六、流式思考输出

开启流式输出时，reasoning_content 和 content 均以增量 delta 形式返回，且 delta.reasoning_content 一定先于 delta.content 出现，需分别处理：

PythonNode.jsfrom openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)stream = client.chat.completions.create( model="mimo-v2.5-pro", messages=[{"role": "user", "content": "分析一下量子计算的优势和挑战"}], max_tokens=4096, stream=True, extra_body={"thinking": {"type": "enabled"}},)print("=== 推理过程（实时）===")answer_started = Falsefor chunk in stream: if not chunk.choices: continue delta = chunk.choices[0].delta reasoning_delta = getattr(delta, "reasoning_content", None) if reasoning_delta: print(reasoning_delta, end="", flush=True) if delta.content: if not answer_started: print("\\n\\n=== 最终答案（实时）===") answer_started = True print(delta.content, end="", flush=True)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const stream = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [{ role: "user", content: "分析一下量子计算的优势和挑战" }], max_tokens: 4096, stream: true, // @ts-ignore thinking: { type: "enabled" },});let answerStarted = false;process.stdout.write("=== 推理过程（实时）===\\n");for await (const chunk of stream) { const delta = chunk.choices[0]?.delta; if (!delta) continue; const reasoning = (delta as any).reasoning_content; if (reasoning) process.stdout.write(reasoning); if (delta.content) { if (!answerStarted) { process.stdout.write("\\n\\n=== 最终答案（实时）===\\n"); answerStarted = true; } process.stdout.write(delta.content); }}

十七、多轮工具调用回传 reasoning_content

开启思考模式且历史会话中存在工具调用时，后续每一轮请求中带有 tool_calls 的 assistant 消息建议完整回传 reasoning_content 字段，以保持模型的最佳表现。实测在 TokenHub 侧缺失该字段不会导致接口报错，但小米建议回传，避免历史推理内容缺失造成上下文不完整。

注意：警告：

注意：历史 reasoning_content 一旦缺失，模型上下文将不完整，即使未直接报错也可能出现指令遵循能力下降、幻觉增多等现象。使用 OpenAI SDK 时，建议直接把响应返回的 assistant 消息对象原样追加到 messages，不要手动重建或裁剪字段。

正确的回传格式如下（assistant 消息同时携带 content、reasoning_content 与 tool_calls）：

{ "model": "mimo-v2.5-pro", "messages": [ {"role": "user", "content": "北京今天天气怎么样？"}, { "role": "assistant", "content": "", "reasoning_content": "用户询问北京天气，需要调用 get_weather 工具获取实时数据。", "tool_calls": [{ "id": "call_xxx", "type": "function", "function": {"name": "get_weather", "arguments": "{\\"city\\": \\"北京\\"}"} }] }, {"role": "tool", "tool_call_id": "call_xxx", "content": "晴，气温28℃，湿度50%"}, {"role": "user", "content": "那明天呢？"} ]}

注意：说明：

注意：通过 TRAE、Cursor、Codex、OpenClaw、OpenCode、Kilo Code 等 AI 编程工具接入时，工具侧一般已实现 reasoning_content 回传逻辑，无需额外处理；若自行开发 Agent 应用，请务必按上述格式处理。

十八、JSON 模式

设置 response_format 为 json_object 可以确保模型输出合法的 JSON 字符串，适合数据抽取、表单填充、分类打标等需要结构化数据的场景。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "system", "content": "只返回 JSON，不要附带任何解释、注释或 Markdown 代码块。格式：{\\"cities\\": [{\\"name\\": string, \\"province\\": string, \\"population\\": number}]}"}, {"role": "user", "content": "返回三座中国城市的信息"} ], "max_tokens": 2048, "response_format": {"type": "json_object"} }'import jsonfrom openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="mimo-v2.5-pro", messages=[ { "role": "system", "content": ( "只返回 JSON，不要附带任何解释、注释或 Markdown 代码块。\\n" '格式：{"cities": [{"name": string, "province": string, "population": number}]}\\n' "未知字段请填充 null。" ), }, {"role": "user", "content": "返回三座中国城市的信息"}, ], max_tokens=2048, response_format={"type": "json_object"},)try: result = json.loads(response.choices[0].message.content) print(json.dumps(result, ensure_ascii=False, indent=2))except json.JSONDecodeError as e: print(f"JSON 解析失败：{e}") print(f"原始内容：{response.choices[0].message.content}")import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const response = await client.chat.completions.create({ model: "mimo-v2.5-pro", messages: [ { role: "system", content: '只返回 JSON，不要附带任何解释、注释或 Markdown 代码块。格式：{"cities": [{"name": string, "province": string, "population": number}]}', }, { role: "user", content: "返回三座中国城市的信息" }, ], max_tokens: 2048, response_format: { type: "json_object" },});const result = JSON.parse(response.choices[0].message.content);console.log(JSON.stringify(result, null, 2));JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 2048);body.put("response_format", new JSONObject().put("type", "json_object"));body.put("messages", new JSONArray() .put(new JSONObject().put("role", "system").put("content", "只返回 JSON，不要附带任何解释。")) .put(new JSONObject().put("role", "user").put("content", "返回三座中国城市的信息")));// … 发送请求，解析返回的 JSON 字符串body := map[string]interface{}{ "model": "mimo-v2.5-pro", "max_tokens": 2048, "response_format": map[string]string{"type": "json_object"}, "messages": []map[string]string{ {"role": "system", "content": "只返回 JSON，不要附带任何解释。"}, {"role": "user", "content": "返回三座中国城市的信息"}, },}// … 发送请求

十九、Anthropic Messages 协议调用

MiMo 系列模型支持 Anthropic Messages 协议，可直接使用 Anthropic SDK 或兼容客户端（Claude Code、OpenClaw、Cline 等）接入。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/messages \\ -H "Content-Type: application/json" \\ -H "x-api-key: YOUR_API_KEY" \\ -H "anthropic-version: 2023-06-01" \\ -d '{ "model": "mimo-v2.5-pro", "max_tokens": 2048, "system": "你是一个专业的技术助手，回答简洁准确。", "messages": [ {"role": "user", "content": "介绍一下 MoE 架构的优势"} ] }'# pip install anthropicfrom anthropic import Anthropicclient = Anthropic( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com",)message = client.messages.create( model="mimo-v2.5-pro", max_tokens=2048, system="你是一个专业的技术助手，回答简洁准确。", messages=[ {"role": "user", "content": "介绍一下 MoE 架构的优势"} ],)print(message.content[0].text)// npm install @anthropic-ai/sdkimport Anthropic from "@anthropic-ai/sdk";const client = new Anthropic({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com",});const message = await client.messages.create({ model: "mimo-v2.5-pro", max_tokens: 2048, system: "你是一个专业的技术助手，回答简洁准确。", messages: [ { role: "user", content: "介绍一下 MoE 架构的优势" } ],});console.log(message.content[0].text);import okhttp3.*;import org.json.*;OkHttpClient httpClient = new OkHttpClient();JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("max_tokens", 2048);body.put("system", "你是一个专业的技术助手，回答简洁准确。");body.put("messages", new JSONArray() .put(new JSONObject().put("role", "user").put("content", "介绍一下 MoE 架构的优势")));Request request = new Request.Builder() .url("https://tokenhub.tencentmaas.com/v1/messages") .addHeader("x-api-key", "YOUR_API_KEY") .addHeader("anthropic-version", "2023-06-01") .addHeader("Content-Type", "application/json") .post(RequestBody.create(body.toString(), MediaType.get("application/json"))) .build();try (Response response = httpClient.newCall(request).execute()) { JSONObject result = new JSONObject(response.body().string()); System.out.println(result.getJSONArray("content").getJSONObject(0).getString("text"));}body := map[string]interface{}{ "model": "mimo-v2.5-pro", "max_tokens": 2048, "system": "你是一个专业的技术助手，回答简洁准确。", "messages": []map[string]string{ {"role": "user", "content": "介绍一下 MoE 架构的优势"}, },}data, _ := json.Marshal(body)req, _ := http.NewRequest("POST", "https://tokenhub.tencentmaas.com/v1/messages", bytes.NewBuffer(data))req.Header.Set("x-api-key", "YOUR_API_KEY")req.Header.Set("anthropic-version", "2023-06-01")req.Header.Set("Content-Type", "application/json")// … 发送请求，从 content[0].text 读取回复

20、Responses API 协议调用

MiMo 系列模型支持 OpenAI Responses 协议，可用于新版 Codex 等基于 Responses 接口的客户端接入。请求路径为 /v1/responses，认证方式与 Chat Completions 一致。

注意：说明：

注意：Responses 协议使用 input 传入对话内容、max_output_tokens 控制输出长度（与 Chat Completions 的 messages、max_tokens 相对应）。完整字段说明与兼容性范围请参见 OpenAI Response 协议字段说明 与 Responses API 兼容模式说明。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/responses \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "mimo-v2.5-pro", "input": "介绍一下 MoE 架构的优势", "max_output_tokens": 2048 }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.responses.create( model="mimo-v2.5-pro", input="介绍一下 MoE 架构的优势", max_output_tokens=2048,)print(response.output_text)import OpenAI from "openai";const client = new OpenAI({ apiKey: "YOUR_API_KEY", baseURL: "https://tokenhub.tencentmaas.com/v1",});const response = await client.responses.create({ model: "mimo-v2.5-pro", input: "介绍一下 MoE 架构的优势", max_output_tokens: 2048,});console.log(response.output_text);JSONObject body = new JSONObject();body.put("model", "mimo-v2.5-pro");body.put("input", "介绍一下 MoE 架构的优势");body.put("max_output_tokens", 2048);Request request = new Request.Builder() .url("https://tokenhub.tencentmaas.com/v1/responses") .addHeader("Authorization", "Bearer YOUR_API_KEY") .addHeader("Content-Type", "application/json") .post(RequestBody.create(body.toString(), MediaType.get("application/json"))) .build();// … 发送请求，从 output 数组中读取文本内容body := map[string]interface{}{ "model": "mimo-v2.5-pro", "input": "介绍一下 MoE 架构的优势", "max_output_tokens": 2048,}data, _ := json.Marshal(body)req, _ := http.NewRequest("POST", "https://tokenhub.tencentmaas.com/v1/responses", bytes.NewBuffer(data))req.Header.Set("Authorization", "Bearer YOUR_API_KEY")req.Header.Set("Content-Type", "application/json")// … 发送请求，从 output 数组中读取文本内容

21、与其他模型的关键差异

22、推荐参数与最佳实践

23、使用限制

24、相关文档

·语言模型调用概览：TokenHub 语言模型通用调用文档，包含 BaseURL、API Key、多轮对话、Function Calling、Anthropic 协议等通用说明。

·OpenAI Chat Completions 协议字段说明：Chat Completions 协议的完整请求与响应字段说明。

·OpenAI Response 协议字段说明：Responses 协议的完整请求与响应字段说明。

·Anthropic Message 协议字段说明：Anthropic Messages 协议的完整请求与响应字段说明。

·深度思考：TokenHub 各模型深度思考能力的通用说明与参数对照。