大模型服务平台 TokenHub MiniMax 调用指南

二、支持的模型

TokenHub 当前支持以下 MiniMax 模型（具体以 模型列表 为准）：

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、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": "minimax-m3", "messages": [ {"role": "user", "content": "你好，请介绍一下你自己"} ], "max_tokens": 1024 }'# 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="minimax-m3", messages=[ {"role": "user", "content": "你好，请介绍一下你自己"} ], max_tokens=1024,)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: "minimax-m3", messages: [ { role: "user", content: "你好，请介绍一下你自己" } ], max_tokens: 1024,});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", "minimax-m3");body.put("max_tokens", 1024);JSONArray messages = new JSONArray();JSONObject userMsg = new JSONObject();userMsg.put("role", "user");userMsg.put("content", "你好，请介绍一下你自己");messages.put(userMsg);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": "minimax-m3", "messages": []map[string]string{ {"role": "user", "content": "你好，请介绍一下你自己"}, }, "max_tokens": 1024, } 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"])}

六、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": "minimax-m3", "messages": [ {"role": "user", "content": "介绍一下大语言模型"} ], "max_tokens": 1024 }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="minimax-m3", messages=[ {"role": "user", "content": "介绍一下大语言模型"} ], max_tokens=1024,)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: "minimax-m3", messages: [ { role: "user", content: "介绍一下大语言模型" } ], max_tokens: 1024,});console.log(response.choices[0].message.content);import okhttp3.*;import org.json.*;OkHttpClient httpClient = new OkHttpClient();JSONObject body = new JSONObject();body.put("model", "minimax-m3");body.put("max_tokens", 1024);JSONArray messages = new JSONArray();messages.put(new JSONObject().put("role", "user").put("content", "介绍一下大语言模型"));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"));}body := map[string]interface{}{ "model": "minimax-m3", "messages": []map[string]string{ {"role": "user", "content": "介绍一下大语言模型"}, }, "max_tokens": 1024,}// … 其余请求代码同快速开始示例

七、流式输出

将 stream 设置为 true 开启 SSE 流式输出，适合长文本生成场景，可有效避免超时，改善用户体验。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "minimax-m3", "messages": [ {"role": "user", "content": "写一首关于春天的短诗"} ], "max_tokens": 512, "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="minimax-m3", messages=[ {"role": "user", "content": "写一首关于春天的短诗"} ], max_tokens=512, 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: "minimax-m3", messages: [ { role: "user", content: "写一首关于春天的短诗" } ], max_tokens: 512, 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", "minimax-m3");body.put("max_tokens", 512);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); String content = json.getJSONArray("choices").getJSONObject(0) .getJSONObject("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": "minimax-m3", "messages": []map[string]string{{"role": "user", "content": "写一首关于春天的短诗"}}, "max_tokens": 512, "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) }}

八、System Prompt

通过 system 角色消息设置模型的行为指令和背景信息。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "minimax-m3", "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": 512 }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="minimax-m3", 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=512,)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: "minimax-m3", 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: 512,});console.log(response.choices[0].message.content);JSONObject body = new JSONObject();body.put("model", "minimax-m3");body.put("max_tokens", 512);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": "minimax-m3", "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": 512,}// … 发送请求代码同快速开始

九、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。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "minimax-m3", "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": 256 }'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="minimax-m3", messages=conversation, max_tokens=1024, ) reply = response.choices[0].message.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: "minimax-m3", messages: conversation, max_tokens: 1024, }); 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", "minimax-m3");body.put("messages", messages);body.put("max_tokens", 1024);// … 发送请求代码同上body := map[string]interface{}{ "model": "minimax-m3", "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": 1024,}// … 发送请求代码同快速开始

十、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": "minimax-m3", "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"] } } }] }'# 第二轮：将工具执行结果传回（tool_call_id 替换为实际返回的 id）curl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "minimax-m3", "messages": [ {"role": "user", "content": "北京今天天气怎么样？"}, {"role": "assistant", "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="minimax-m3", messages=messages, tools=tools,)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：将工具结果传回模型 messages.append(assistant_message) messages.append({ "role": "tool", "tool_call_id": tool_call.id, "content": tool_result, }) final_response = client.chat.completions.create( model="minimax-m3", messages=messages, tools=tools, ) 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: "minimax-m3", messages, tools,});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%"; messages.push(assistantMsg); messages.push({ role: "tool", tool_call_id: toolCall.id, content: toolResult }); const response2 = await client.chat.completions.create({ model: "minimax-m3", messages, tools, }); 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", "minimax-m3");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，执行工具，构造第二轮请求body := map[string]interface{}{ "model": "minimax-m3", "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，构造第二轮请求

十一、思考模式

MiniMax 系列模型内置思考能力。省略 thinking 参数时，默认开启 adaptive thinking，响应会包含思考内容。对于 M2.x 模型，thinking 无法关闭。思考内容默认以 <think>…</think> 标签嵌入在响应的 content 字段中；如需将思考内容拆分到独立字段，可在请求体中添加 "reasoning_split": true。

十二、thinking 参数说明

十三、使用 reasoning_split 拆分思考内容

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "minimax-m3", "messages": [ {"role": "user", "content": "解方程 x^2 – 5x + 6 = 0"} ], "max_tokens": 2048, "reasoning_split": true }'from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://tokenhub.tencentmaas.com/v1",)response = client.chat.completions.create( model="minimax-m3", messages=[{"role": "user", "content": "解方程 x^2 – 5x + 6 = 0"}], max_tokens=2048, extra_body={"reasoning_split": True}, # 拆分思考内容到独立字段)msg = response.choices[0].message# reasoning_split=True 时，推理过程在独立字段中返回reasoning = getattr(msg, "reasoning_content", None)if reasoning: print("=== 推理过程 ===") print(reasoning)print("=== 最终答案 ===")print(msg.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: "minimax-m3", messages: [{ role: "user", content: "解方程 x^2 – 5x + 6 = 0" }], max_tokens: 2048, // @ts-ignore – reasoning_split 为扩展字段 reasoning_split: true,});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", "minimax-m3");body.put("max_tokens", 2048);body.put("reasoning_split", true);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": "minimax-m3", "max_tokens": 2048, "reasoning_split": true, "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

十四、响应结构说明

不传 reasoning_split（默认）：思考内容以 <think> 标签嵌入 content 中：

{ "choices": [{ "message": { "role": "assistant", "content": "<think>\\n因式分解：(x-2)(x-3) = 0，所以 x = 2 或 x = 3。\\n</think>\\n方程 x² – 5x + 6 = 0 的解为：**x = 2** 或 **x = 3**" } }]}

传 reasoning_split: true：思考内容拆分到独立的 reasoning_content 和 reasoning_details 字段：

{ "choices": [{ "message": { "role": "assistant", "reasoning_content": "因式分解：(x-2)(x-3) = 0，所以 x = 2 或 x = 3。", "reasoning_details": […], "content": "方程 x² – 5x + 6 = 0 的解为：**x = 2** 或 **x = 3**" }, "finish_reason": "stop" }], "usage": { "completion_tokens": 120, "completion_tokens_details": { "reasoning_tokens": 80 } }}

十五、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，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="minimax-m3", messages=[{"role": "user", "content": "分析一下量子计算的优势和挑战"}], max_tokens=2048, stream=True, extra_body={"reasoning_split": True},)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: "minimax-m3", messages: [{ role: "user", content: "分析一下量子计算的优势和挑战" }], max_tokens: 2048, stream: true, // @ts-ignore reasoning_split: true,});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); }}

十六、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，适合需要结构化数据的场景。

注意：使用 JSON 模式时，必须在 system 或 user 消息中明确要求模型输出 JSON 格式，否则可能导致模型一直输出空内容。

cURLPythonNode.jsJavaGocurl https://tokenhub.tencentmaas.com/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer YOUR_API_KEY" \\ -d '{ "model": "minimax-m3", "messages": [ {"role": "system", "content": "请以 JSON 格式返回结果。"}, {"role": "user", "content": "返回三座中国城市的信息，每个包含 name、province、population 字段"} ], "max_tokens": 512, "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="minimax-m3", messages=[ {"role": "system", "content": "请以 JSON 格式返回结果。"}, { "role": "user", "content": "返回三座中国城市的信息，每个包含 name、province、population 字段", }, ], max_tokens=512, response_format={"type": "json_object"},)result = json.loads(response.choices[0].message.content)print(json.dumps(result, ensure_ascii=False, indent=2))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: "minimax-m3", messages: [ { role: "system", content: "请以 JSON 格式返回结果。" }, { role: "user", content: "返回三座中国城市的信息，每个包含 name、province、population 字段", }, ], max_tokens: 512, 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", "minimax-m3");body.put("max_tokens", 512);body.put("response_format", new JSONObject().put("type", "json_object"));body.put("messages", new JSONArray() .put(new JSONObject().put("role", "system").put("content", "请以 JSON 格式返回结果。")) .put(new JSONObject().put("role", "user").put("content", "返回三座中国城市的信息，每个包含 name、province、population 字段")));// … 发送请求，解析返回的 JSON 字符串body := map[string]interface{}{ "model": "minimax-m3", "max_tokens": 512, "response_format": map[string]string{"type": "json_object"}, "messages": []map[string]string{ {"role": "system", "content": "请以 JSON 格式返回结果。"}, {"role": "user", "content": "返回三座中国城市的信息，每个包含 name、province、population 字段"}, },}// … 发送请求

十七、与其他模型的关键差异

十八、推荐参数与最佳实践

十九、使用限制

20、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。