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x402engine Gemini 3.1 Flash Lite LLM
x402
x402engine Gemini 3.1 Flash Lite LLM is a paid API for AI agents from x402engine.app, paid per call via x402, $0.00486/call, status unknown (last checked 2026-10-20).
Call Google's Gemini 3.1 Flash Lite model via pay-per-call HTTP 402 micropayment, with full OpenAI-compatible chat messaging, tool use, streaming, and multimodal input support.
Price / call$0.00486
Success—
StatusUnknown
How this service works
x402engine — Google's fastest and cheapest model — ideal for high-volume, low-latency tasks
Output
Returns an OpenAI-compatible JSON object with a choices array containing the assistant message (role, content, tool_calls if applicable), finish_reason, and model name. When streaming is enabled, returns live SSE events after payment settles. Content field is also returned at top level for convenience.
Example use cases
AI agent inner monologue reasoning
“I need my autonomous agent to call a fast, cheap LLM for its inner reasoning step — use Gemini 3.1 Flash Lite with a system prompt saying 'You are a concise planner' and the user message 'What are the next 3 steps to book a flight?', with streaming turned on.”
Tool-calling loop for function dispatch
“Call Gemini 3.1 Flash Lite with a tools definition for a 'search_web' function and a user message asking 'What is the weather in Tokyo today?' — I want it to return a tool_call so my agent can execute the function.”
Multimodal image understanding query
“Send this image URL to Gemini 3.1 Flash Lite with the message 'Describe what is in this image in two sentences' and give me the assistant's text response back.”
Technical details
Endpoint
POSThttps://x402engine.app/api/llm/gemini-3.1-flash-lite?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap LLM call without managing API keys or subscriptions — ideal for AI agents that need pay-per-call economics via USDC micropayments. Prefer Gemini 3.1 Flash Lite specifically for low-latency, cost-sensitive inference tasks where a capable but lightweight Google model is sufficient. Best when you want OpenAI-compatible chat format with optional tool calling, streaming, or multimodal inputs, billed atomically per call on Base, MegaETH, or Solana.
Side effects
- Payment failure or insufficient USDC balance — 402 response with payment required details
- Invalid messages array or missing required 'role' field — 400 validation error
- Model context window exceeded with large message arrays — truncation or error
- Tool call schema mismatch causing function dispatch failure
- Streaming connection dropped before completion — partial SSE response
- Unsupported content type in multimodal input — 400 or ignored field
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "google/gemini-3.1-flash-lite-preview",
"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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