FarOut Pay-Per-Call LLM Inference (GPT-5.6-sol) via x402

x402

FarOut Pay-Per-Call LLM Inference (GPT-5.6-sol) via x402 is a paid API for AI agents from farouter.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).

Send a chat/completion request to GPT-5.6-sol (or any of 17 frontier models) and pay per call in USDC on Base via x402 — no API key or account required.

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

Fixed-price chat completions for gpt-5.6-sol on FarOut: flat $0.005 per call (includes up to 4,000 input + 1,000 output tokens). Rate: $0.380/1M input tokens, $2.280/1M output tokens. Pay with USDC on Base using x402. No API key, no account, no prepaid balance.

Output

Returns an OpenAI-compatible JSON object with a choices array (each entry has a message with role and content, plus finish_reason) and a usage object showing prompt_tokens and completion_tokens consumed.

Example use cases

Autonomous agent coding assistant

“I need you to call FarOut's GPT-5.6-sol model to write a Python script that fetches stock prices from a public API and plots them — keep it under 800 tokens output and use temperature 0.3.”

On-demand reasoning without account setup

“Use FarOut to send this logic puzzle to DeepSeek V4 and get a step-by-step solution — I don't have an API key so use the x402 pay-per-call route, max 1024 tokens.”

Multi-model comparison for chat response

“Send the same customer support question to both Gemini 3.1 and Kimi K3 on FarOut, with max_tokens 300 and temperature 0.7, so I can compare their answers side by side.”

Technical details

Endpoint

POSThttps://farouter.tech/v1/models/gpt-5.6-sol/chat/completions

When to prefer

Choose FarOut when your agent needs to call frontier LLMs without pre-registering an account, storing an API key, or maintaining a prepaid credit balance — ideal for autonomous agents that pay per request in USDC via x402. Prefer this over OpenAI direct when you want crypto-native per-call billing, access to multiple frontier models (GPT, Gemini, DeepSeek, Kimi, GLM, MiniMax) through one endpoint, or when operating in a trustless/permissionless environment where key management is undesirable.

Side effects

Payment insufficient or x402 transaction rejected — request fails before inference begins
Model ID not found — returns error if model string doesn't match a supported model from GET /v1/models
max_tokens or max_completion_tokens exceeds model context limit — inference may be truncated or rejected
Malformed messages array (missing role or content) — schema validation error
Streaming (SSE) connection drops mid-response — partial output may be returned
Rate limiting or upstream model provider outage — temporary 5xx errors

Input

Schema · Request body

Field	Type	Description
model	string	Model id from GET /v1/models, e.g. glm-5.3. No provider prefix.
stream	boolean	SSE streaming.
messages	array	Chat messages, [OI]-compatible {role, content}.
max_tokens	integer	Output token budget. Sets your spending cap; actual usage is what gets settled (true-up).
temperature	number	Sampling temperature.
max_completion_tokens	integer	Alias of max_tokens (gpt-5.x models).

Output

Schema · Response

{
  "type": "json",
  "example": {
    "usage": {
      "prompt_tokens": 6,
      "completion_tokens": 8
    },
    "choices": [
      {
        "message": {
          "role": "assistant",
          "content": "Hello! How can I help?"
        },
        "finish_reason": "stop"
      }
    ]
  }
}

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How this service works

Output

Example use cases

Autonomous agent coding assistant

“I need you to call FarOut's GPT-5.6-sol model to write a Python script that fetches stock prices from a public API and plots them — keep it under 800 tokens output and use temperature 0.3.”

On-demand reasoning without account setup

“Use FarOut to send this logic puzzle to DeepSeek V4 and get a step-by-step solution — I don't have an API key so use the x402 pay-per-call route, max 1024 tokens.”

Multi-model comparison for chat response

“Send the same customer support question to both Gemini 3.1 and Kimi K3 on FarOut, with max_tokens 300 and temperature 0.7, so I can compare their answers side by side.”

Technical details

Endpoint

POSThttps://farouter.tech/v1/models/gpt-5.6-sol/chat/completions

When to prefer

Side effects

Payment insufficient or x402 transaction rejected — request fails before inference begins
Model ID not found — returns error if model string doesn't match a supported model from GET /v1/models
max_tokens or max_completion_tokens exceeds model context limit — inference may be truncated or rejected
Malformed messages array (missing role or content) — schema validation error
Streaming (SSE) connection drops mid-response — partial output may be returned
Rate limiting or upstream model provider outage — temporary 5xx errors

Input

Schema · Request body

Field	Type	Description
model	string	Model id from GET /v1/models, e.g. glm-5.3. No provider prefix.
stream	boolean	SSE streaming.
messages	array	Chat messages, [OI]-compatible {role, content}.
max_tokens	integer	Output token budget. Sets your spending cap; actual usage is what gets settled (true-up).
temperature	number	Sampling temperature.
max_completion_tokens	integer	Alias of max_tokens (gpt-5.x models).

Output

Schema · Response

{
  "type": "json",
  "example": {
    "usage": {
      "prompt_tokens": 6,
      "completion_tokens": 8
    },
    "choices": [
      {
        "message": {
          "role": "assistant",
          "content": "Hello! How can I help?"
        },
        "finish_reason": "stop"
      }
    ]
  }
}