X402 Cloud Gemini 3.5 Flash Inference Endpoint

x402

X402 Cloud Gemini 3.5 Flash Inference Endpoint is a paid API for AI agents from api.x402cloud.space, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-02).

Runs a pay-per-call Gemini 3.5 Flash text generation request via x402 micropayment, returning AI-generated content candidates

Price / call$0.003

Seller volume →—

Success—

StatusUnknown

How this service works

X402 Cloud AI Inference Endpoints is an agent-ready x402 API provider for Gemini, image, audio, video, realtime, and specialized AI inference workflows. We expose pay-per-call USDC endpoints designed for autonomous AI agents, application backends, trading bots, creative automation, and RAG systems with some of the lowest x402 AI inference prices available in the market.

Output

A JSON object containing a candidates array with generated text parts inside content.parts[].text, plus a usageMetadata object with token counts. The primary response is the model-generated string under candidates[0].content.parts[0].text.

Example use cases

Example prompt

“Using the x402 Gemini 3.5 Flash endpoint, generate a 200-word product description for a noise-cancelling headphone — keep the output under 512 tokens.”

Technical details

Endpoint

POSThttps://api.x402cloud.space/gemini-3.5-flash?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need Gemini 3.5 Flash inference on a pay-per-call basis without a Google Cloud subscription, especially in autonomous agent pipelines, trading bots, or RAG systems that use x402 micropayments in USDC. Prefer this over direct Google APIs when you want per-call billing settled on-chain and no monthly commitment.

Side effects

402 Payment Required if x402 payment header is missing or USDC balance insufficient
400 Bad Request if prompt is empty or exceeds 32000 character limit
429 Too Many Requests if rate limit exceeded
500 Internal Server Error if upstream Gemini API is unavailable
Invalid maxOutputTokens if value exceeds 8192 or is less than 1

Input

{
  "prompt": "Write a brief technical summary of how machine learning models are trained and optimized for inference performance.",
  "maxOutputTokens": 256
}

Output

{
  "candidates": [
    {
      "content": {
        "role": "model"
      },
      "finishReason": "MAX_TOKENS"
    }
  ],
  "createTime": "2026-06-16T17:41:51.585167Z",
  "responseId": "34oxas_bI7mn7rcPsOrdmAE",
  "modelVersion": "gemini-3.5-flash",
  "usageMetadata": {
    "trafficType": "ON_DEMAND",
    "totalTokenCount": 270,
    "promptTokenCount": 18,
    "thoughtsTokenCount": 243,
    "promptTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 18
      }
    ],
    "candidatesTokenCount": 9,
    "candidatesTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 9
      }
    ]
  }
}

Schema · Response

{
  "type": "json",
  "example": {
    "candidates": [
      {
        "content": {
          "parts": [
            {
              "text": "Generated response"
            }
          ]
        }
      }
    ]
  },
  "outputSchema": {
    "type": "object",
    "properties": {
      "candidates": {
        "type": "array"
      },
      "usageMetadata": {
        "type": "object"
      }
    }
  }
}

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