qwen/qwen3.7-max
प्रकाशन तिथि: 2026-05-21
1,000,000 context · $2.50/M input tokens · $7.50/M output tokens
Qwen3.7-Max is Alibaba’s flagship model in the Qwen3.7 series, built for agent-centric text workflows. It is optimized for coding, debugging, office automation, productivity tasks, tool use, and long-horizon autonomous execution. With a 1M-token context window and up to 64K output tokens, it is well suited for large documents, repository-scale coding, multi-step planning, structured generation, and workflows that require sustained reasoning across hundreds or thousands of steps.
Pricing
Pay-per-use
No upfront costs, pay only for what you use
Try the model
API Usage
Use the following code examples to integrate with our API:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["WAVESPEED_API_KEY"],
base_url="https://llm.wavespeed.ai/v1",
timeout=120.0,
max_retries=2,
)
try:
response = client.chat.completions.create(
model="qwen/qwen3.7-max",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content or "")
except Exception as exc:
raise SystemExit(f"LLM request failed: {exc}") from excModel Introduction
Qwen: Qwen3.7 Max
Qwen3.7-Max is Alibaba’s flagship model in the Qwen3.7 series, designed for agent-centric text workflows. It is optimized for coding, debugging, office automation, productivity tasks, tool use, and long-horizon autonomous execution.
Why It Looks Great
- Flagship Qwen3.7 model built for agentic workloads
- Strong fit for coding, debugging, office automation, productivity tasks, and tool use
- 1M-token context window for long prompts, large documents, codebases, and multi-turn workflows
- Up to 64K output tokens for extended reasoning, coding, and structured generation
- Designed for long-horizon autonomous execution across complex multi-step tasks
- Function calling and tool-use support for agentic application workflows
- Structured output support for JSON responses and schema-constrained generation
- Reasoning controls for tuning latency, quality, and cost per request
Key Features
- Context Window: 1,000,000 tokens
- Max Input: 934,464 tokens
- Max Output: 65,536 tokens
- Input: Text
- Output: Text
- Vision: Not listed
- Function Calling: Supported
- Structured Outputs: Supported
- Thinking Mode: Supported
- Image Generation: Not listed
- Audio Input: Not listed
- Supported Parameters: include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Specifications
| Specification | Value |
|---|---|
| Provider | alibaba |
| Model Type | Chat Completions model |
| Architecture | text->text |
| Context Window | 1,000,000 tokens |
| Max Input | 934,464 tokens |
| Max Output | 65,536 tokens |
| Input | Text |
| Output | Text |
| Vision | Not listed |
| Function Calling | Supported |
| Structured Outputs | Supported |
| Thinking Mode | Supported |
| Primary Use Cases | Coding, office automation, productivity workflows, long-horizon agents, tool use |
| Release | May 2026 |
Pricing
| Token Type | Cost |
|---|---|
| Input | $2.50 per million tokens |
| Output | $7.50 per million tokens |
| Cache Write | $3.125 per million tokens |
How to Use
- Write your prompt - describe the task, provide context, and specify the desired output format.
- Submit - the model processes your request and returns the response.
API Integration
Base URL: https://llm.wavespeed.ai/v1
API Endpoint: chat/completions
Model ID: qwen/qwen3.7-max
API Usage
Python SDK
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="qwen/qwen3.7-max",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)cURL
curl https://llm.wavespeed.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "qwen/qwen3.7-max",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: qwen/qwen3.7-max
- Provider: alibaba
- Best suited for coding agents, office automation, long-context text workflows, multi-step productivity tasks, tool use, and structured output generation
Info
Supported Functionality
API Access Guide
Qwen3.7 Max API
qwen/qwen3.7-max
Qwen3.7-Max is Alibaba’s flagship model in the Qwen3.7 series, built for agent-centric text workflows. It is optimized for coding, debugging, office automation, productivity tasks, tool use, and long-horizon autonomous execution. With a 1M-token context window and up to 64K output tokens, it is well suited for large documents, repository-scale coding, multi-step planning, structured generation, and workflows that require sustained reasoning across hundreds or thousands of steps.
Input
$2.5 /M
Output
$7.5 /M
Context
1000K
Max Output
66K
Tool Use
Supported
Try Qwen3.7 Max on WaveSpeedAI
Access Qwen3.7 Max through our unified API — OpenAI-compatible, no cold starts, transparent pricing.
Frequently Asked Questions about Qwen3.7 Max
How much does Qwen3.7 Max cost via the API?+
Pricing on WaveSpeedAI: $2.50 per million input tokens and $7.50 per million output tokens. Prompt caching and batch processing are billed separately and reduce effective cost on long, repetitive workloads.
What is the context window of Qwen3.7 Max?+
Qwen3.7 Max supports up to 1000K tokens of context with up to 66K tokens of output per request.
Is Qwen3.7 Max OpenAI-compatible?+
WaveSpeedAI exposes Qwen3.7 Max at https://llm.wavespeed.ai/v1 through the OpenAI-compatible Chat Completions interface. Most OpenAI SDK clients work by changing the base URL and API key; optional fields depend on the selected model.
How do I get started with Qwen3.7 Max?+
Sign in to WaveSpeedAI, create an API key in Access Keys, then send a request to https://llm.wavespeed.ai/v1/chat/completions with the model id shown above. Check the current model catalog for availability, capabilities, and pricing.