DeepSeek-V4-Pro - Qwen Cloud

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Text GenerationReasoning

Overview

Text GenerationReasoning

A flagship MoE large model with 1.6 trillion parameters and 49 billion activated parameters, natively supporting context lengths of up to one million tokens. Trained on a vast corpus of high-quality data, it excels in advanced mathematical reasoning, complex logical inference, specialized coding, and deep analysis of long-form text, making it well-suited for demanding applications such as cutting-edge research, sophisticated office workflows, and advanced AI agents.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$2.4Per 1M tokens
Output
$4.8Per 1M tokens
Input(Implicit Cache)
$0.2Per 1M tokens

Context

Max Input

Max Output

393.21K

Rate Limits

RPMRequests Per Minute
10K
TPMTokens Per Minute
1.20M

API Reference

Get API Key

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import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
response = Generation.call(
    # If the environment variable is not set, replace it with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="deepseek-v4-pro",
    messages=messages,
    result_format="message",
    # Enable deep thinking
    enable_thinking=True,
)

if response.status_code == 200:
    # Print thinking process
    print("=" * 20 + "Thinking process" + "=" * 20)
    print(response.output.choices[0].message.reasoning_content)
    
    # Print response
    print("=" * 20 + "Full response" + "=" * 20)
    print(response.output.choices[0].message.content)
else:
    print(f"HTTP return code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")

import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
response = Generation.call(
    # If the environment variable is not set, replace it with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="deepseek-v4-pro",
    messages=messages,
    result_format="message",
    # Enable deep thinking
    enable_thinking=True,
)

if response.status_code == 200:
    # Print thinking process
    print("=" * 20 + "Thinking process" + "=" * 20)
    print(response.output.choices[0].message.reasoning_content)
    
    # Print response
    print("=" * 20 + "Full response" + "=" * 20)
    print(response.output.choices[0].message.content)
else:
    print(f"HTTP return code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")