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SignalHarness Model Output Cost Comparator (Caller-Supplied Usage)
x402
SignalHarness Model Output Cost Comparator (Caller-Supplied Usage) is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-15).
Compares LLM model output costs based on caller-supplied usage data, returning a structured cost analysis across models.
Price / call$0.015
Success—
StatusUnknown
How this service works
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Output
Returns a JSON analysis object containing a cost comparison derived from the caller-supplied usage data, along with warnings noting that results depend on the accuracy of the submitted data. Includes service metadata, a receipt with payment details, and an analysis_json field containing the structured comparison results.
Example use cases
Cheapest model for production chatbot
“I'm running a customer support chatbot that processes about 50,000 input tokens and 15,000 output tokens per day. Can you compare the costs across GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Flash using those numbers and tell me which model would save me the most money?”
Budget audit for AI pipeline
“I need to audit our AI spending — here's our monthly usage: 2 million prompt tokens and 800,000 completion tokens spread across GPT-4 Turbo and Mistral Large. Can you run a cost comparison on that usage and show me if we'd save money switching providers?”
Model selection for new agent build
“I'm planning a new agent that will use roughly 3,000 input tokens and 1,200 output tokens per call, running about 500 times a day. Can you compare the total daily costs across the top LLMs so I can decide which model to build on?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/model_output_cost_compare_from_supplied_usage/invokeWhen to prefer
Choose this endpoint when you have your own token usage data and want a structured, automated cost comparison across LLM providers without manually consulting each provider's pricing page. Ideal for agents optimizing model selection, auditing AI spend, or making build-vs-buy decisions based on real usage patterns. Prefer over manual lookups when you need machine-readable output with receipt provenance.
Side effects
- Invalid or malformed request_json input results in validation error
- Missing or empty request_json field returns schema error
- Caller-supplied usage data is inaccurate leading to misleading cost comparison (warned in response)
- Payment failure via x402 protocol prevents execution
- Model identifiers not recognized may result in incomplete comparison
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_cost_compare_from_supplied_usage",
"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_cost_compare_from_supplied_usage",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "15000",
"resultSha256": "6919303e542fe79c8f493a960e0ee11db4c49a6932d026aa74e6b109bb2d6a32",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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