Qwen3.8-Max

Qwen3.8-Max

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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

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

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_interpreterResponses API

i2i_searchResponses API

t2i_searchResponses API

web_extractorResponses API

web_searchResponses API

API Reference #

Call API

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from openai import OpenAI
import os

client = OpenAI(
    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)

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from openai import OpenAI
import os

client = OpenAI(
    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)