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SignalHarness Model Output Format Comparator
x402
SignalHarness Model Output Format Comparator is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-14).
Compares and analyzes model output formats from caller-supplied JSON data to surface structural and schema differences
Price / call$0.015
Success—
StatusUnknown
How this service works
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Output
Returns an analysis_json object containing the comparison findings between the supplied model output formats, along with any warnings about data reliability. The response also includes a receipt with payment metadata (paymentId, amountAtomic, network, latency) and an evidence_scope field indicating the data source was caller-supplied.
Example use cases
LLM output schema standardization audit
“I have JSON samples from GPT-4 and Claude — can you compare their output formats and flag any structural mismatches or incompatibilities I should know about?”
Multi-model pipeline compatibility check
“We're building a pipeline that routes between two models — run a format comparison on these two output JSON samples and tell me if the schemas are aligned enough to use interchangeably.”
AI response format regression check
“Our model was updated last week and I want to check if the output format changed — here's a JSON blob with the old and new response structures, please compare them and highlight any differences.”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/model_output_format_compare/invokeWhen to prefer
Use this endpoint when you need a quick, low-cost ($0.015 USDC) programmatic comparison of two or more AI model output format schemas — especially in pipelines where you need to verify cross-model compatibility, detect output drift after model updates, or standardize response structures. Prefer this over manual inspection or custom diffing logic when you want a structured analysis result with audit-trail receipts on Base USDC.
Side effects
- Malformed or invalid JSON in request_json returns a parse error
- request_json below minimum length (2 chars) or above maximum (65536 chars) is rejected
- Missing required request_json field causes a validation error
- Caller-supplied data that is structurally ambiguous may trigger warnings in the analysis
- Empty or trivially small JSON may produce low-confidence analysis results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "model_output_format_compare",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "model_output_format_compare",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "15000",
"resultSha256": "174530b0091f3e0b45d79c56aaa8ee5164494253f20b0885763703ba534700a8",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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