Qwen-Plus

Copied!

Try AIAdd to Compare

ReasoningText Generation

Overview

ReasoningText Generation

Qwen-Plus is an enhanced version of the Qwen ultra-large language model that supports multiple input languages such as Chinese and English. Compared to previous versions, it shows significant improvements in both Chinese and English code generation, logical reasoning, and multilingual abilities. The response style has been greatly adjusted to align with human preferences, with noticeable enhancements in the level of detail and clarity of responses. Specialized improvements have been made in creative writing, adherence to JSON formatting, and role-playing abilities.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$0.4Per 1M tokens
Output
$1.2Per 1M tokens
Input(Thinking)
$0.4Per 1M tokens
Output(Thinking)
$4Per 1M tokens
Input(Implicit Cache)
$0.08Per 1M tokens
Input(Thinking Implicit Cache)
$0.08Per 1M tokens
Thinking Output(Batch File)
$2Per 1M tokens
Input(Batch File)
$0.2Per 1M tokens
Output(Batch File)
$0.6Per 1M tokens
Input(Thinking Batch File)
$0.2Per 1M tokens
Explicit Cache Creation
$0.5Per 1M tokens
Explicit Cache Read
$0.04Per 1M tokens
Explicit Cache Creation(Thinking)
$0.5Per 1M tokens
Explicit Cache Read(Thinking)
$0.04Per 1M tokens

Input
$0.4Per 1M tokens
Output
$1.2Per 1M tokens
Input(Thinking)
$0.4Per 1M tokens
Output(Thinking)
$4Per 1M tokens
Input(Implicit Cache)
$0.08Per 1M tokens
Input(Thinking Implicit Cache)
$0.08Per 1M tokens
Thinking Output(Batch File)
$2Per 1M tokens
Input(Batch File)
$0.2Per 1M tokens
Output(Batch File)
$0.6Per 1M tokens
Input(Thinking Batch File)
$0.2Per 1M tokens
Explicit Cache Creation
$0.5Per 1M tokens
Explicit Cache Read
$0.04Per 1M tokens
Explicit Cache Creation(Thinking)
$0.5Per 1M tokens
Explicit Cache Read(Thinking)
$0.04Per 1M tokens

Context

Max Input

995.90K

Max Output

32.76K

Rate Limits

RPMRequests Per Minute
600
TPMTokens Per Minute
1M

API Reference

Get API Key

Copied!

123456789101112131415161718192021222324252627282930

from openai import OpenAI
import os

client = OpenAI(
    # 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"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]
completion = client.chat.completions.create(
    model="qwen-plus",  # You can replace this with another deep thinking models
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True
)
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20)
for chunk in completion:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full response" + "=" * 20)
            is_answering = True
        print(delta.content, end="", flush=True)

from openai import OpenAI
import os

client = OpenAI(
    # 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"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]
completion = client.chat.completions.create(
    model="qwen-plus",  # You can replace this with another deep thinking models
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True
)
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20)
for chunk in completion:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full response" + "=" * 20)
            is_answering = True
        print(delta.content, end="", flush=True)