Model Marketplace

Qwen3.5-Plus

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Overview

The Qwen3.5 native vision-language series Plus model has seen a substantial improvement in agentic coding capabilities compared to the February 15th snapshot. Inference speed has also been significantly enhanced, while its knowledge retention, reasoning ability, and long-context processing remain at a high level, making it well-suited for complex agent-based tasks. It is ideal for applications such as coding agents, production workflows, and high-throughput scenarios. This version is based on a snapshot taken on April 20, 2026.

Input

ImageTextVideo

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Input
    $0.4Per 1M tokens
  • Output
    $2.4Per 1M tokens
  • Explicit Cache Creation
    $0.5Per 1M tokens
  • Explicit Cache Read
    $0.04Per 1M tokens

Rate Limits & Context

  • Max Input
    991K
  • Max Output
    65K
  • Max Input (Thinking)
    983K
  • Max Output (Thinking)
    65K
  • Context
    1M
  • TPMTokens Per Minute
    1M
  • RPMRequests Per Minute
    600

Built-in Tools

web_searchResponses API
code_interpreterResponses API
web_extractorResponses API
i2i_searchResponses API
t2i_searchResponses API

API Reference

Call API
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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://maas.qwencloudapi.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]
completion = client.chat.completions.create(
    model="qwen3.5-plus-2026-04-20",  # 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)