Model Marketplace

Qwen3.7-Max

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Overview

The early version of the Max model in the Qwen3.7 series supports only the reasoning mode and makes its plain-text capabilities available for experimentation. Qwen3.7 is a next‑generation flagship model designed for the agent‑centric era, with major improvements in coding and agent‑level capabilities. This release is a snapshot as of May 17, 2026.

Input

Text

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
    $2.5Per 1M tokens
  • Output
    $7.5Per 1M tokens

Rate Limits & Context

  • Max Input
    991K
  • Max Output
    131K
  • Max Input (Thinking)
    983K
  • Max Output (Thinking)
    131K
  • Context
    1M
  • Max Reasoning
    262K
  • TPMTokens Per Minute
    1M
  • RPMRequests Per Minute
    600

Built-in Tools

code_interpreterResponses API
web_extractorResponses API
web_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="qwen3.7-max-2026-05-17",
    messages=messages,
    stream=True
)
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20)
for chunk in completion:
	if chunk.choices:
		delta = chunk.choices[0].delta
		# Collect only the thinking content
		if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
			if not is_answering:
				print(delta.reasoning_content, end="", flush=True)
		# When content is received, start generating the response
		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)