# Qwen3.8-Max

> Source: <https://www.qwencloud.com/models/qwen3.8-max-0902>
> Published: 2026-09-02 06:46:55+00:00

### Qwen3.8-Max

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[Try AI](https://www.qwencloud.com/try-ai/chat?models=qwen3.8-max-0902)

[Add to Compare](/compare?models=qwen3.8-max-0902)

## Overview

Qwen3.8-Max-0902（alias qwen3.8-max-2026-09-02）is an upgraded snapshot of qwen3.8-max. Coding capability breaks new ground, handling more complex engineering-scale projects and long-horizon autonomous development. Collaborative agent performance is significantly enhanced, with greater composure in multi-tool orchestration and end-to-end task delivery. Native vision understanding is refined across chart reasoning, document parsing, and multimodal perception — sharper and more reliable. Retains the 1M context window, thinking mode, and full tool ecosystem, evolving at a higher level of intelligence.

#### 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](https://docs.qwencloud.com/developer-guides/text-generation/partial-mode)

#### Function Calling

Use function calling to connect large language models with external tools and systems.[View docs](https://docs.qwencloud.com/developer-guides/text-generation/function-calling)

#### Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.[View docs](https://docs.qwencloud.com/developer-guides/text-generation/context-cache#implicit-cache)

#### Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.[View docs](https://docs.qwencloud.com/developer-guides/text-generation/structured-output)

## Pricing

- Input$2Per 1M tokens
- Output$6Per 1M tokens
- Input(Implicit Cache)$0.25Per 1M tokens
- Explicit Cache Creation$2.5Per 1M tokens
- Explicit Cache Read$0.17Per 1M tokens

## Rate Limits & Context

- Max Input991K
- Max Output131K
- Max Input (Thinking)983K
- Max Output (Thinking)131K
- Context1M
- Max Reasoning262K
- TPMTokens Per Minute1M
- RPMRequests Per Minute15K

## Built-in Tools

[code_interpreter](https://docs.qwencloud.com/developer-guides/text-generation/code-interpreter)Responses API

[i2i_search](https://docs.qwencloud.com/developer-guides/text-generation/image-search)Responses API

[t2i_search](https://docs.qwencloud.com/developer-guides/text-generation/image-search)Responses API

[web_extractor](https://docs.qwencloud.com/developer-guides/text-generation/web-scraping)Responses API

[web_search](https://docs.qwencloud.com/developer-guides/text-generation/web-search)Responses API

## API Reference

[Call API](https://home.qwencloud.com/api-keys)

Copy success!

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``` python
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="qwen3.8-max-0902",  # 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)
```

123456789101112131415161718192021222324252627282930

``` python
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="qwen3.8-max-0902",  # 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)
```