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x402engine Qwen3.7-Max LLM
x402
x402engine Qwen3.7-Max LLM is a paid API for AI agents from x402engine.app, paid per call via x402, $0.014078/call, status unknown (last checked 2026-10-12).
Runs inference on Alibaba's Qwen3.7-Max large language model via a pay-per-call HTTP 402 micropayment endpoint
Price / call$0.014078
Success—
StatusUnknown
How this service works
125 production APIs behind HTTP 402 micropayments. 87 LLMs, Kling V3 and Hailuo video generation, image generation, code execution, travel search, web scraping, TTS, and transaction simulation. Pay with USDC on Base, USDm on MegaETH, or USDC on Solana.
Output
Returns an OpenAI-compatible JSON response containing the model name ('qwen/qwen3.7-max'), a choices array with the assistant message (role, content, optional tool_calls), finish_reason, and a top-level content field. When streaming is enabled, returns OpenAI-compatible SSE chunks after payment settlement.
Example use cases
Multilingual reasoning with Qwen
“I need to analyze this Chinese business contract and summarize the key obligations in English — use Qwen3.7-Max since it's particularly strong on Chinese-language tasks. Here's the contract text: [contract text].”
Function-calling agent loop
“Use Qwen3.7-Max to run my agent loop — give it this system prompt 'You are a travel assistant', pass in the user message 'Find me flights to Tokyo', and include my get_flights function definition so it can call it.”
Pay-per-call LLM for cost control
“I want to run a one-off question through Qwen3.7-Max without setting up a subscription — just charge my USDC wallet per call. Ask it: 'Explain the differences between transformer and mamba architectures in three paragraphs.'”
Technical details
Endpoint
POSThttps://x402engine.app/api/llm/qwen3.7-max?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need Alibaba's Qwen3.7-Max specifically — a strong performer on Chinese-language tasks, multilingual reasoning, and coding — and want pay-per-call USDC micropayment billing without committing to a subscription. Prefer it over OpenAI or Anthropic endpoints when Qwen's architecture or training data is a better fit, or when you want to compare outputs across LLM providers on a per-call basis.
Side effects
- Payment failure — insufficient USDC/USDm balance results in HTTP 402 not settled
- Invalid messages array — missing required 'role' or 'content'/'tool_calls' fields returns validation error
- Token limit exceeded — very long conversations may be truncated or rejected
- Unsupported parameter for model — seed, top_k, logprobs may not be supported by all model configurations
- Streaming timeout — SSE connection may drop on slow networks after payment is made
- Rate limiting — excessive concurrent calls may be throttled
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "qwen/qwen3.7-max",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello!"
},
"finish_reason": "stop"
}
],
"content": "Hello!"
}
}Tags
x402
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Zero review prompt (fires after every successful call)
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