Qwen-QwQ-Plus

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Reasoning

Overview

Reasoning

Qwen QwQ Reasoning Model Enhanced Edition, based on the Qwen2.5 model training QwQ reasoning model, through reinforcement learning greatly improved the model's reasoning ability. The core indicators of the model such as mathematics code (AIME 24/25, livecodebench) and some general indicators (IFEval, LiveBench, etc.) have reached the level of DeepSeek-R1 full-blooded edition.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$0.8Per 1M tokens
Output
$2.4Per 1M tokens

Context

131.07K

Max Input

98.30K

Max Output

8.19K

Rate Limits

RPMRequests Per Minute
60
TPMTokens Per Minute
100K

API Reference

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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": "user", "content": "Who are you"}]

completion = Generation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwq-plus",
    messages=messages,
    result_format="message", 
    stream=True,
    incremental_output=True, 
)

is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in completion:
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        if (
            chunk.output.choices[0].message.reasoning_content != ""
            and chunk.output.choices[0].message.content == ""
        ):
            print(chunk.output.choices[0].message.reasoning_content, end="", flush=True)
        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, end="", flush=True)

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

messages = [{"role": "user", "content": "Who are you"}]

completion = Generation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwq-plus",
    messages=messages,
    result_format="message", 
    stream=True,
    incremental_output=True, 
)

is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in completion:
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        if (
            chunk.output.choices[0].message.reasoning_content != ""
            and chunk.output.choices[0].message.content == ""
        ):
            print(chunk.output.choices[0].message.reasoning_content, end="", flush=True)
        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, end="", flush=True)