Model penalaran visual menghasilkan proses berpikirnya sebelum memberikan jawaban. Gunakan model ini untuk tugas visual kompleks seperti soal matematika, analisis grafik, atau pemahaman video.
Showcase
Komponen di atas hanya untuk keperluan demonstrasi dan tidak mengirim permintaan yang sebenarnya.
Model yang didukung
-
Qwen3.8
- Model hybrid-thinking: qwen3.8-max, qwen3.8-flash
-
Qwen3.7
- Model hybrid-thinking: qwen3.7-plus, qwen3.7-plus-2026-05-26, qwen3.7-max-2026-06-08, qwen3.7-flash, qwen3.7-flash-2026-07-15
-
Qwen3.6
- Model hybrid-thinking: qwen3.6-plus, qwen3.6-plus-2026-04-02, qwen3.6-flash, qwen3.6-flash-2026-04-16, qwen3.6-35b-a3b
-
Qwen3.5
- Model hybrid-thinking: qwen3.5-plus, qwen3.5-plus-2026-02-15, qwen3.5-flash, qwen3.5-flash-2026-02-23, qwen3.5-397b-a17b, qwen3.5-122b-a10b, qwen3.5-27b, qwen3.5-35b-a3b
-
Qwen3-VL
- Model hybrid-thinking: qwen3-vl-plus, qwen3-vl-plus-2025-12-19, qwen3-vl-plus-2025-09-23, qwen3-vl-flash, qwen3-vl-flash-2025-10-15
- Model thinking-only: qwen3-vl-235b-a22b-thinking, qwen3-vl-32b-thinking, qwen3-vl-30b-a3b-thinking, qwen3-vl-8b-thinking
-
QVQ
- Model thinking-only: qvq-max series, qvq-plus series
-
Kimi
- Model hybrid-thinking: kimi-k2.6, kimi-k2.5
Panduan penggunaan
-
Proses berpikir: Model Studio menyediakan dua jenis model penalaran visual: hybrid-thinking dan thinking-only.
-
Model hybrid-thinking: Kendalikan proses berpikir dengan parameter
enable_thinking:- Diatur ke
true: menghasilkan proses berpikir terlebih dahulu, lalu respons akhir (default untuk seri Qwen3.5 dan seterusnya). - Diatur ke
false: langsung menghasilkan respons (default untuk seriqwen3-vl-plus,qwen3-vl-flash).
- Diatur ke
- Model thinking-only: Model ini selalu menghasilkan proses berpikir sebelum memberikan respons, dan perilaku ini tidak dapat dinonaktifkan.
-
Model hybrid-thinking: Kendalikan proses berpikir dengan parameter
-
Metode output: Gunakan streaming untuk mencegah timeout akibat proses berpikir yang panjang.
- Qwen3.8, Qwen3.7, Qwen3.6, Qwen3.5, Qwen3-VL, kimi-k2.6, kimi-k2.5, dan stepfun/step-3.7-flash mendukung metode streaming dan non-streaming.
- Seri QVQ hanya mendukung keluaran streaming.
-
Rekomendasi prompt sistem:
- Konversasi single-turn/sederhana: Jangan atur
System Message. Berikan instruksi (seperti peran, format) melaluiUser Messageuntuk hasil inferensi terbaik. - Aplikasi kompleks (agen, pemanggilan tool): Gunakan
System Messageuntuk menentukan peran model, kemampuan, dan kerangka perilaku.
- Konversasi single-turn/sederhana: Jangan atur
Mulai
Prasyarat- Kunci API telah dibuat dan diekspor sebagai variabel lingkungan.
- Pengguna SDK: instal versi terbaru (DashScope Python SDK ≥1.24.6, DashScope Java SDK ≥2.21.10).
qvq-max untuk menyelesaikan soal matematika dari gambar. Contoh ini menggunakan streaming untuk mencetak proses berpikir dan respons akhir secara terpisah.
- Kompatibel dengan OpenAI
- DashScope
- Python
- Node.js
- HTTP
Copy
from openai import OpenAI
import os
# Inisialisasi klien OpenAI
client = OpenAI(
# Kunci API berbeda berdasarkan wilayah. Untuk mendapatkannya, lihat https://modelstudio.console.alibabacloud.com/?tab=model#/api-key
# Jika belum dikonfigurasi, ganti dengan: api_key="sk-xxx"
api_key = os.getenv("DASHSCOPE_API_KEY"),
# Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)
reasoning_content = "" # Menyimpan proses berpikir lengkap
answer_content = "" # Menyimpan respons lengkap
is_answering = False # Memeriksa apakah proses berpikir telah selesai dan respons dimulai
# Buat permintaan chat completion
completion = client.chat.completions.create(
model="qvq-max", # Contoh menggunakan qvq-max. Ganti dengan nama model lain sesuai kebutuhan.
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
},
},
{"type": "text", "text": "How do I solve this problem?"},
],
},
],
stream=True,
# Hapus komentar berikut untuk mengembalikan penggunaan token pada chunk terakhir
# stream_options={
# "include_usage": True
# }
)
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
for chunk in completion:
# Jika chunk.choices kosong, cetak penggunaan
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Cetak proses berpikir
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Mulai merespons
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
is_answering = True
# Cetak proses respons
print(delta.content, end='', flush=True)
answer_content += delta.content
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)
Copy
import OpenAI from "openai";
import process from 'process';
// Inisialisasi klien OpenAI
const openai = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY, // Baca dari variabel lingkungan. Kunci API berbeda berdasarkan wilayah. Untuk mendapatkannya, lihat https://modelstudio.console.alibabacloud.com/?tab=model#/api-key
// Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1'
});
let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let messages = [
{
role: "user",
content: [
{ type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
{ type: "text", text: "Solve this problem" },
]
}]
async function main() {
try {
const stream = await openai.chat.completions.create({
model: 'qvq-max',
messages: messages,
stream: true
});
console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.repeat(20) + '\n');
for await (const chunk of stream) {
if (!chunk.choices?.length) {
console.log('\nUsage:');
console.log(chunk.usage);
continue;
}
const delta = chunk.choices[0].delta;
// Tangani proses berpikir
if (delta.reasoning_content) {
process.stdout.write(delta.reasoning_content);
reasoningContent += delta.reasoning_content;
}
// Tangani respons formal
else if (delta.content) {
if (!isAnswering) {
console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
isAnswering = true;
}
process.stdout.write(delta.content);
answerContent += delta.content;
}
}
} catch (error) {
console.error('Error:', error);
}
}
main();
Klik untuk melihat proses berpikir dan respons lengkap
Klik untuk melihat proses berpikir dan respons lengkap
Copy
====================Thinking process====================
Okay, I need to solve this problem about the surface area and volume of a rectangular prism and a cube. First, I need to carefully look at the problem to make sure I understand the requirements for each part.
The problem asks to calculate the surface area and volume of two shapes separately. The first shape is a rectangular prism, and the second is a cube. The units are all in centimeters, and each question is worth 6 points, for a total of 12 points. This looks like a math homework problem, possibly from elementary or middle school geometry.
First, I need to recall the formulas for the surface area and volume of a rectangular prism and a cube. For a rectangular prism, the formula for surface area is: \( 2(ab + bc + ac) \), where a, b, and c are the length, width, and height, respectively. The volume is \( abc \). For a cube, because all side lengths are equal, the surface area is \( 6a^2 \), and the volume is \( a^3 \).
Now, let's look at the first shape, a rectangular prism with given dimensions of length 4 cm, width 3 cm, and height 2 cm. I need to calculate its surface area first. Plugging the values into the formula:
Surface Area = \( 2(4×3 + 3×2 + 4×2) \)
= \( 2(12 + 6 + 8) \)
= \( 2×26 \)
= 52 cm²
Next is the volume, again plugging in the values:
Volume = \( 4×3×2 \)
= 24 cm³
Then, for the second shape, a cube with a side length of 3 cm. The surface area is calculated as follows:
Surface Area = \( 6×3^2 \)
= \( 6×9 \)
= 54 cm²
And the volume is:
Volume = \( 3^3 \)
= 27 cm³
However, I need to check for any potential mistakes. For example, in calculating the surface area of the rectangular prism, did I miscalculate the area of any face? Or did I make a mistake in the multiplication?
For instance, the three different faces of the rectangular prism are 4×3, 3×2, and 4×2. The areas of these faces are 12, 6, and 8. Adding them up gives 26, and multiplying by 2 gives 52, which should be correct. For the volume, 4×3=12, and then multiplying by 2 is 24, which is also correct.
For the cube, each face is 3×3=9, and with six faces, the total is 54. The volume is 3×3×3=27, which is also correct.
So the final answers should be:
1. The surface area of the rectangular prism is 52 square centimeters, and the volume is 24 cubic centimeters.
2. The surface area of the cube is 54 square centimeters, and the volume is 27 cubic centimeters.
However, I need to confirm if there is any other hidden information or potential traps in the figures in the problem. For example, sometimes problems might include dashed lines or shaded areas, but in this case, the rectangular prism and cube in the image look standard, with no special markings. Therefore, I believe my calculations are correct.
====================Full response====================
### Solution:
**1. Rectangular Prism (length 4 cm, width 3 cm, height 2 cm)**
- **Surface Area**:
\[
2 \times (4 \times 3 + 3 \times 2 + 4 \times 2) = 2 \times (12 + 6 + 8) = 2 \times 26 = 52 \, \text{cm}^2
\]
- **Volume**:
\[
4 \times 3 \times 2 = 24 \, \text{cm}^3
\]
**2. Cube (side length 3 cm)**
- **Surface Area**:
\[
6 \times 3^2 = 6 \times 9 = 54 \, \text{cm}^2
\]
- **Volume**:
\[
3^3 = 27 \, \text{cm}^3
\]
**Answer:**
1. The surface area of the rectangular prism is \(52 \, \text{cm}^2\), and its volume is \(24 \, \text{cm}^3\).
2. The surface area of the cube is \(54 \, \text{cm}^2\), and its volume is \(27 \, \text{cm}^3\).
Copy
# ======= IMPORTANT =======
# Replace {WorkspaceId} with your workspace ID. URLs vary by region.
# API keys differ by region. To obtain one, see https://modelstudio.console.alibabacloud.com/?tab=model#/api-key
# === Delete this comment before execution ===
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qvq-max",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
}
},
{
"type": "text",
"text": "Solve this problem"
}
]
}
],
"stream":true,
"stream_options":{"include_usage":true}
}'
Klik untuk melihat proses berpikir dan respons lengkap
Klik untuk melihat proses berpikir dan respons lengkap
Copy
data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
data: {"choices":[{"finish_reason":null,"delta":{"content":null,"reasoning_content":"Okay"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
data: {"choices":[{"delta":{"content":null,"reasoning_content":","},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
data: {"choices":[{"delta":{"content":null,"reasoning_content":" I am now"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
data: {"choices":[{"delta":{"content":null,"reasoning_content":" going to"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
data: {"choices":[{"delta":{"content":null,"reasoning_content":" solve"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
.....
data: {"choices":[{"delta":{"content":"square "},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
data: {"choices":[{"delta":{"content":"centimeters"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":544,"completion_tokens":590,"total_tokens":1134,"completion_tokens_details":{"text_tokens":590},"prompt_tokens_details":{"text_tokens":24,"image_tokens":520}},"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
data: [DONE]
Model QVQ melalui DashScope:
incremental_outputdefault ketrue(tidak dapat dinonaktifkan; hanya streaming).result_formatdefault ke"message"(tidak dapat diatur ke"text").
- Python
- Java
- HTTP
Copy
import os
import dashscope
from dashscope import MultiModalConversation
# Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
messages = [
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "How do I solve this problem?"}
]
}
]
response = MultiModalConversation.call(
# Kunci API berbeda berdasarkan wilayah. Untuk mendapatkannya, lihat https://modelstudio.console.alibabacloud.com/?tab=model#/api-key
# Jika variabel lingkungan belum dikonfigurasi, ganti baris berikut dengan Kunci API Studio Model Anda: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model="qvq-max", # Contoh menggunakan qvq-max. Ganti dengan nama model lain sesuai kebutuhan.
messages=messages,
stream=True,
)
# Menyimpan proses berpikir lengkap
reasoning_content = ""
# Menyimpan respons lengkap
answer_content = ""
# Memeriksa apakah proses berpikir telah selesai dan respons dimulai
is_answering = False
print("=" * 20 + "Thinking process" + "=" * 20)
for chunk in response:
# Jika proses berpikir dan respons keduanya kosong, abaikan
message = chunk.output.choices[0].message
reasoning_content_chunk = message.get("reasoning_content", None)
if (chunk.output.choices[0].message.content == [] and
reasoning_content_chunk == ""):
pass
else:
# Jika saat ini sedang dalam proses berpikir
if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
print(chunk.output.choices[0].message.reasoning_content, end="")
reasoning_content += chunk.output.choices[0].message.reasoning_content
# Jika saat ini sedang dalam respons
elif chunk.output.choices[0].message.content != []:
if not is_answering:
print("\n" + "=" * 20 + "Full response" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content[0]["text"], end="")
answer_content += chunk.output.choices[0].message.content[0]["text"]
# Untuk mencetak proses berpikir dan respons lengkap, hapus komentar dan jalankan kode berikut
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")
Klik untuk melihat proses berpikir dan respons lengkap
Klik untuk melihat proses berpikir dan respons lengkap
Copy
====================Thinking process====================
Okay, I need to solve this problem about the surface area and volume of a rectangular prism and a cube. First, I need to carefully look at the problem to make sure I understand the requirements for each part.
The problem asks to calculate the surface area and volume of two shapes separately. The first shape is a rectangular prism, and the second is a cube. The units are all in centimeters, and each question is worth 6 points, for a total of 12 points. This looks like a math homework problem, possibly from elementary or middle school geometry.
First, I need to recall the formulas for the surface area and volume of a rectangular prism and a cube. For a rectangular prism, the formula for surface area is: \( Area = 2(ab + bc + ac) \), where a, b, and c are the length, width, and height, respectively. The volume is: \( Volume = abc \). For a cube, because all side lengths are equal, the surface area is: \( Area = 6a^2 \), and the volume is: \( Volume = a^3 \).
Now, let's look at the first shape, a rectangular prism with given dimensions of length 4 cm, width 3 cm, and height 2 cm. I need to first confirm that these values correspond correctly to the variables in the formula. Usually, the three dimensions of a rectangular prism can be named arbitrarily, but for convenience, we can consider the longest side as the length, the middle one as the width, and the shortest as the height. However, in this case, the problem has clearly labeled the lengths of each side, so I can use them directly.
Next, I'll calculate the surface area of the first rectangular prism. Plugging the values into the formula:
\( Area = 2(4×3 + 3×2 + 4×2) \)
First, calculate each term inside the parentheses:
\( 4×3 = 12 \)
\( 3×2 = 6 \)
\( 4×2 = 8 \)
Then, add these results together:
\( 12 + 6 + 8 = 26 \)
And multiply by 2:
\( 2 × 26 = 52 \)
So, the surface area of the first rectangular prism is 52 square centimeters.
Next, calculate the volume:
\( Volume = 4 × 3 × 2 = 24 \)
So, the volume is 24 cubic centimeters.
Now, let's look at the second shape, a cube with all side lengths of 3 cm. Therefore, the surface area calculation is:
\( Area = 6 × 3^2 = 6 × 9 = 54 \)
And the volume is:
\( Volume = 3^3 = 27 \)
So, the surface area of the cube is 54 square centimeters, and the volume is 27 cubic centimeters.
During the calculation, I need to pay attention to the consistency of units. The unit given in the problem is centimeters, so the final results should be in square centimeters and cubic centimeters. Also, I need to ensure there are no calculation errors, such as the order of multiplication and addition, especially when calculating the surface area of the rectangular prism, where it's easy to miss a term or make a calculation mistake.
Additionally, I'll check for any other possible misunderstandings, such as whether the length, width, and height are correctly identified. But in this problem, because the lengths of each side are clearly labeled, this should not be an issue. Also, for the cube, all side lengths are equal, so there's no complexity from different side lengths.
To summarize, the surface area of the first rectangular prism is 52 square centimeters, and its volume is 24 cubic centimeters. The surface area of the second cube is 54 square centimeters, and its volume is 27 cubic centimeters.
====================Full response====================
### Solution:
**1. Rectangular Prism (length 4 cm, width 3 cm, height 2 cm)**
- **Surface Area**:
\[
Area = 2(ab + bc + ac) = 2(4×3 + 3×2 + 4×2) = 2(12 + 6 + 8) = 2×26 = 52 \, \text{cm}^2
\]
- **Volume**:
\[
Volume = abc = 4×3×2 = 24 \, \text{cm}^3
\]
**2. Cube (side length 3 cm)**
- **Surface Area**:
\[
Area = 6a^2 = 6×3^2 = 6×9 = 54 \, \text{cm}^2
\]
- **Volume**:
\[
Volume = a^3 = 3^3 = 27 \, \text{cm}^3
\]
**Answer:**
1. The surface area of the rectangular prism is \(52 \, \text{cm}^2\), and its volume is \(24 \, \text{cm}^3\).
2. The surface area of the cube is \(54 \, \text{cm}^2\), and its volume is \(27 \, \text{cm}^3\).
Copy
// DashScope SDK version >= 2.19.0
import java.util.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.exception.InputRequiredException;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";
}
private static final Logger logger = LoggerFactory.getLogger(Main.class);
private static StringBuilder reasoningContent = new StringBuilder();
private static StringBuilder finalContent = new StringBuilder();
private static boolean isFirstPrint = true;
private static void handleGenerationResult(MultiModalConversationResult message) {
String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
String reasoning = Objects.isNull(re)?"":re; // Default value
List<Map<String, Object>> content = message.getOutput().getChoices().get(0).getMessage().getContent();
if (!reasoning.isEmpty()) {
reasoningContent.append(reasoning);
if (isFirstPrint) {
System.out.println("====================Thinking process====================");
isFirstPrint = false;
}
System.out.print(reasoning);
}
if (Objects.nonNull(content) && !content.isEmpty()) {
Object text = content.get(0).get("text");
finalContent.append(content.get(0).get("text"));
if (!isFirstPrint) {
System.out.println("\n====================Full response====================");
isFirstPrint = true;
}
System.out.print(text);
}
}
public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg) {
return MultiModalConversationParam.builder()
// Kunci API berbeda berdasarkan wilayah. Untuk mendapatkannya, lihat https://modelstudio.console.alibabacloud.com/?tab=model#/api-key
// Jika belum dikonfigurasi, ganti dengan: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
// Contoh menggunakan qvq-max. Ganti dengan nama model lain sesuai kebutuhan.
.model("qvq-max")
.messages(Arrays.asList(Msg))
.incrementalOutput(true)
.build();
}
public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
Flowable<MultiModalConversationResult> result = conv.streamCall(param);
result.blockingForEach(message -> {
handleGenerationResult(message);
});
}
public static void main(String[] args) {
try {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessage userMsg = MultiModalMessage.builder()
.role(Role.USER.getValue())
.content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
Collections.singletonMap("text", "Solve this problem")))
.build();
streamCallWithMessage(conv, userMsg);
// Cetak hasil akhir
// if (reasoningContent.length() > 0) {
// System.out.println("\n====================Full response====================");
// System.out.println(finalContent.toString());
// }
} catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
logger.error("An exception occurred: {}", e.getMessage());
}
System.exit(0);
}
}
Klik untuk melihat proses berpikir dan respons lengkap
Klik untuk melihat proses berpikir dan respons lengkap
Copy
====================Thinking process====================
Hmm, I need to solve this problem, which is to calculate the surface area and volume of two shapes. First, I need to carefully examine the image provided in the problem. The first shape is a rectangular prism, and the second is a cube. The problem asks to calculate their surface area and volume separately, with units in centimeters.
Let's look at the first shape, the rectangular prism. Its dimensions should be length, width, and height. According to the labels on the image, the length is 4 cm, the width is 3 cm, and the height is 2 cm. Right? I remember the formula for the surface area of a rectangular prism is 2 times (length × width + length × height + width × height). And the volume is length times width times height. Let me double-check if the formula is correct. Yes, the surface area is indeed the sum of the areas of the six faces, and because opposite faces have equal areas, this formula is correct.
So, plugging in the values, the surface area should be 2×(4×3 + 4×2 + 3×2). First, calculate the terms inside the parentheses: 4×3=12, 4×2=8, 3×2=6. Adding them up gives 12+8+6=26. Then multiplying by 2 gives 52 square centimeters. For the volume, 4×3×2=24 cubic centimeters. This part should be correct.
Next is the second shape, the cube. All its side lengths are 3 cm. The surface area of a cube is 6 times the square of the side length, because it has six identical square faces. The volume is the cube of the side length. So the surface area should be 6×3²=6×9=54 square centimeters. The volume is 3³=27 cubic centimeters. I need to pay attention to the units here. The problem states the unit is cm, so the results should be written in square centimeters and cubic centimeters.
However, I should double-check if I made any mistakes. For example, are the sides of the rectangular prism correctly identified? In the image, the length of the rectangular prism does look longer than its width, so length is 4, width is 3, and height is 2. For the cube, all three dimensions are 3, which is fine. Did I make any calculation errors? For example, in the surface area calculation for the rectangular prism, are the products correct, and is the addition correct? For instance, 4×3=12, 4×2=8, 3×2=6, adding up to 26, and multiplying by 2 is 52, which is correct. The volume 4×3×2=24 is also correct. For the cube, the surface area 6×9=54 and volume 27 are also correct.
One thing to note is the units. The problem clearly states the unit is cm, so I should add the correct unit symbols to the answers. Also, the problem states that each question is worth 6 points, for a total of 12 points, but there are only two questions, so each is worth 6 points. This doesn't affect the calculation process, but it's a reminder not to miss any steps or units.
To summarize, the surface area of the first shape is 52 square centimeters, and its volume is 24 cubic centimeters; the surface area of the second shape is 54 square centimeters, and its volume is 27 cubic centimeters. That should be it.
====================Full response====================
**Answer:**
1. **Rectangular Prism**
- **Surface Area**: \(2 \times (4 \times 3 + 4 \times 2 + 3 \times 2) = 2 \times 26 = 52\) square centimeters
- **Volume**: \(4 \times 3 \times 2 = 24\) cubic centimeters
2. **Cube**
- **Surface Area**: \(6 \times 3^2 = 6 \times 9 = 54\) square centimeters
- **Volume**: \(3^3 = 27\) cubic centimeters
**Explanation:**
- The surface area of a rectangular prism is obtained by calculating the total area of its six faces, and its volume is the product of its length, width, and height.
- The surface area of a cube is the sum of the areas of its six identical square faces, and its volume is the cube of its side length.
- All units are in centimeters, as required by the problem.
- curl
Copy
# ======= IMPORTANT =======
# Replace {WorkspaceId} with your workspace ID. URLs vary by region.
# API keys differ by region. To obtain one, see https://modelstudio.console.alibabacloud.com/?tab=model#/api-key
# === Delete this comment before execution ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-H 'X-DashScope-SSE: enable' \
-d '{
"model": "qvq-max",
"input":{
"messages":[
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "Solve this problem"}
]
}
]
}
}'
Klik untuk melihat proses berpikir dan respons lengkap
Klik untuk melihat proses berpikir dan respons lengkap
Copy
id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":[],"reasoning_content":"Okay","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":547,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":3,"input_tokens":544,"output_tokens_details":{"text_tokens":3},"image_tokens":520},"request_id":"f361ae45-fbef-9387-9f35-1269780e0864"}
id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":[],"reasoning_content":",","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":548,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":4,"input_tokens":544,"output_tokens_details":{"text_tokens":4},"image_tokens":520},"request_id":"f361ae45-fbef-9387-9f35-1269780e0864"}
id:3
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":[],"reasoning_content":" I am now","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":549,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":5,"input_tokens":544,"output_tokens_details":{"text_tokens":5},"image_tokens":520},"request_id":"f361ae45-fbef-9387-9f35-1269780e0864"}
.....
id:566
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":[{"text":"square"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":1132,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":588,"input_tokens":544,"output_tokens_details":{"text_tokens":588},"image_tokens":520},"request_id":"758b0356-653b-98ac-b4d3-f812437ba1ec"}
id:567
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":[{"text":"centimeters"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":1133,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":589,"input_tokens":544,"output_tokens_details":{"text_tokens":589},"image_tokens":520},"request_id":"758b0356-653b-98ac-b4d3-f812437ba1ec"}
id:568
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":[],"role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":1134,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":590,"input_tokens":544,"output_tokens_details":{"text_tokens":590},"image_tokens":520},"request_id":"758b0356-653b-98ac-b4d3-f812437ba1ec"}
Kemampuan inti
Mengaktifkan atau menonaktifkan proses berpikir
Untuk skenario yang memerlukan proses berpikir detail (pemecahan masalah, analisis laporan), aktifkan mode berpikir menggunakan parameterenable_thinking seperti ditunjukkan di bawah.
- Kompatibel dengan OpenAI
- DashScope
enable_thinking dan thinking_budget adalah parameter non-standar OpenAI. Metode pengiriman parameter berbeda berdasarkan bahasa:- Python SDK: Anda harus mengirimkannya melalui dictionary
extra_body. - Node.js SDK: Anda dapat mengirimkannya langsung sebagai parameter tingkat atas.
import os
from openai import OpenAI
client = OpenAI(
# Kunci API berbeda berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
# Jika Anda menggunakan model di wilayah Beijing, ganti base_url dengan https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)
reasoning_content = "" # Menyimpan proses berpikir lengkap
answer_content = "" # Menyimpan respons lengkap
is_answering = False # Memeriksa apakah proses berpikir telah selesai dan respons dimulai
enable_thinking = True
# Buat permintaan chat completion
completion = client.chat.completions.create(
model="qwen3.5-plus",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
},
},
{"type": "text", "text": "How do I solve this problem?"},
],
},
],
stream=True,
# Parameter enable_thinking mengaktifkan proses berpikir. Parameter thinking_budget menetapkan jumlah maksimum token untuk proses penalaran.
# Untuk qwen3.5-plus, qwen3-vl-plus, dan qwen3-vl-flash, Anda dapat menggunakan enable_thinking untuk mengaktifkan atau menonaktifkan berpikir (qwen3.5-plus diaktifkan secara default). Untuk model dengan akhiran 'thinking', seperti qwen3-vl-235b-a22b-thinking, enable_thinking hanya dapat diatur ke true. Parameter ini tidak berlaku untuk model Qwen-VL lainnya.
extra_body={
'enable_thinking': enable_thinking
},
# Hapus komentar berikut untuk mengembalikan penggunaan token pada chunk terakhir
# stream_options={
# "include_usage": True
# }
)
if enable_thinking:
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
for chunk in completion:
# Jika chunk.choices kosong, cetak penggunaan
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Cetak proses berpikir
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Mulai merespons
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
is_answering = True
# Cetak proses respons
print(delta.content, end='', flush=True)
answer_content += delta.content
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)
import os
import dashscope
from dashscope import MultiModalConversation
# Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
# Jika Anda menggunakan model di wilayah Beijing, ganti base_url dengan https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"
enable_thinking = True
messages = [
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "How do I solve this problem?"}
]
}
]
response = MultiModalConversation.call(
# Jika belum dikonfigurasi, ganti dengan: api_key="sk-xxx",
# Kunci API berbeda berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv('DASHSCOPE_API_KEY'),
model="qwen3.5-plus",
messages=messages,
stream=True,
# Parameter enable_thinking mengaktifkan proses berpikir.
# Untuk qwen3.5-plus, qwen3-vl-plus, dan qwen3-vl-flash, Anda dapat menggunakan enable_thinking untuk mengaktifkan atau menonaktifkan berpikir (qwen3.5-plus diaktifkan secara default). Untuk model dengan akhiran 'thinking', seperti qwen3-vl-235b-a22b-thinking, enable_thinking hanya dapat diatur ke true. Parameter ini tidak berlaku untuk model Qwen-VL lainnya.
enable_thinking=enable_thinking
)
# Menyimpan proses berpikir lengkap
reasoning_content = ""
# Menyimpan respons lengkap
answer_content = ""
# Memeriksa apakah proses berpikir telah selesai dan respons dimulai
is_answering = False
if enable_thinking:
print("=" * 20 + "Thinking process" + "=" * 20)
for chunk in response:
# Jika proses berpikir dan respons keduanya kosong, abaikan
message = chunk.output.choices[0].message
reasoning_content_chunk = message.get("reasoning_content", None)
if (chunk.output.choices[0].message.content == [] and
reasoning_content_chunk == ""):
pass
else:
# Jika saat ini sedang dalam proses berpikir
if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
print(chunk.output.choices[0].message.reasoning_content, end="")
reasoning_content += chunk.output.choices[0].message.reasoning_content
# Jika saat ini sedang dalam respons
elif chunk.output.choices[0].message.content != []:
if not is_answering:
print("\n" + "=" * 20 + "Full response" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content[0]["text"], end="")
answer_content += chunk.output.choices[0].message.content[0]["text"]
# Untuk mencetak proses berpikir dan respons lengkap, hapus komentar dan jalankan kode berikut
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")
Batasi panjang proses berpikir
Gunakan parameterthinking_budget untuk membatasi panjang token proses berpikir. Jika melebihi batas, konten akan dipotong dan model segera menghasilkan jawaban akhir. Nilai default adalah panjang maksimum rantai-pikiran model. Untuk informasi lebih lanjut, lihat Daftar model.
Parameter
thinking_budget didukung oleh Qwen3.8, Qwen3.7, Qwen3.6, Qwen3.5, Qwen3-VL (mode berpikir), kimi-k2.5 (mode berpikir), dan kimi-k2.6 (mode berpikir).- Kompatibel dengan OpenAI
- DashScope
thinking_budget adalah parameter non-standar OpenAI. Saat menggunakan OpenAI Python SDK, kirimkan melalui extra_body.import os
from openai import OpenAI
client = OpenAI(
# Kunci API berbeda berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
# Jika Anda menggunakan model di wilayah Beijing, ganti base_url dengan https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)
reasoning_content = "" # Menyimpan proses berpikir lengkap
answer_content = "" # Menyimpan respons lengkap
is_answering = False # Memeriksa apakah proses berpikir telah selesai dan respons dimulai
enable_thinking = True
# Buat permintaan chat completion
completion = client.chat.completions.create(
model="qwen3.5-plus",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
},
},
{"type": "text", "text": "How do I solve this problem?"},
],
},
],
stream=True,
# Parameter enable_thinking mengaktifkan proses berpikir. Parameter thinking_budget menetapkan jumlah maksimum token untuk proses penalaran.
# Untuk qwen3.5-plus, qwen3-vl-plus, dan qwen3-vl-flash, Anda dapat menggunakan enable_thinking untuk mengaktifkan atau menonaktifkan berpikir (qwen3.5-plus diaktifkan secara default). Untuk model dengan akhiran 'thinking', seperti qwen3-vl-235b-a22b-thinking, enable_thinking hanya dapat diatur ke true. Parameter ini tidak berlaku untuk model Qwen-VL lainnya.
extra_body={
'enable_thinking': enable_thinking,
"thinking_budget": 81920},
# Hapus komentar berikut untuk mengembalikan penggunaan token pada chunk terakhir
# stream_options={
# "include_usage": True
# }
)
if enable_thinking:
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
for chunk in completion:
# Jika chunk.choices kosong, cetak penggunaan
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Cetak proses berpikir
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Mulai merespons
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
is_answering = True
# Cetak proses respons
print(delta.content, end='', flush=True)
answer_content += delta.content
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)
import os
import dashscope
from dashscope import MultiModalConversation
# Ganti {WorkspaceId} dengan ID ruang kerja Anda. URL berbeda berdasarkan wilayah.
# Jika Anda menggunakan model di wilayah Beijing, ganti base_url dengan https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"
enable_thinking = True
messages = [
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "How do I solve this problem?"}
]
}
]
response = MultiModalConversation.call(
# Jika belum dikonfigurasi, ganti dengan: api_key="sk-xxx",
# Kunci API berbeda berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv('DASHSCOPE_API_KEY'),
model="qwen3.5-plus",
messages=messages,
stream=True,
# Parameter enable_thinking mengaktifkan proses berpikir.
# Untuk qwen3.5-plus, qwen3-vl-plus, dan qwen3-vl-flash, Anda dapat menggunakan enable_thinking untuk mengaktifkan atau menonaktifkan berpikir (qwen3.5-plus diaktifkan secara default). Untuk model dengan akhiran 'thinking', seperti qwen3-vl-235b-a22b-thinking, enable_thinking hanya dapat diatur ke true. Parameter ini tidak berlaku untuk model Qwen-VL lainnya.
enable_thinking=enable_thinking,
# Parameter thinking_budget menetapkan jumlah maksimum token untuk proses penalaran.
thinking_budget=81920,
)
# Menyimpan proses berpikir lengkap
reasoning_content = ""
# Menyimpan respons lengkap
answer_content = ""
# Memeriksa apakah proses berpikir telah selesai dan respons dimulai
is_answering = False
if enable_thinking:
print("=" * 20 + "Thinking process" + "=" * 20)
for chunk in response:
# Jika proses berpikir dan respons keduanya kosong, abaikan
message = chunk.output.choices[0].message
reasoning_content_chunk = message.get("reasoning_content", None)
if (chunk.output.choices[0].message.content == [] and
reasoning_content_chunk == ""):
pass
else:
# Jika saat ini sedang dalam proses berpikir
if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
print(chunk.output.choices[0].message.reasoning_content, end="")
reasoning_content += chunk.output.choices[0].message.reasoning_content
# Jika saat ini sedang dalam respons
elif chunk.output.choices[0].message.content != []:
if not is_answering:
print("\n" + "=" * 20 + "Full response" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content[0]["text"], end="")
answer_content += chunk.output.choices[0].message.content[0]["text"]
# Untuk mencetak proses berpikir dan respons lengkap, hapus komentar dan jalankan kode berikut
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")
Contoh lainnya
Model penalaran visual mendukung semua fitur pemahaman visual untuk skenario kompleks seperti:- Pemahaman multi-gambar
- Pemahaman video
- Memproses gambar resolusi tinggi
- Mengirim file lokal (encoding Base64 atau path file)
Tagihan
Total biaya = (Token input × Harga per token input) + (Token output × Harga per token output).- Proses berpikir (
reasoning_content) dikenakan biaya sebagai token output. Jika tidak ada output berpikir, harga mode non-berpikir berlaku. - Untuk perhitungan token gambar/video, lihat Pemahaman gambar dan video.