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How much does an AI agent cost to run Payment Fraud Detection?

Token cost benchmark for an autonomous Payment Fraud Detection agent, across 26 models. Prices as of 26 Jul 2026.

An agent for Payment Fraud Detection on the clean path costs about $0.0089 to $0.625 per outcome depending on the model, around 8.0x the cost of a single chat message. At 10,000 outcomes a month that is roughly $89 to $6,250.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0089$89
Llama 4 Maverick$0.27$0.85$0.0154$154
Gemini 2.5 Flash$0.30$2.50$0.0218$218
DeepSeek V4$0.43$0.87$0.0233$233
GPT-4.1 mini$0.40$1.60$0.0238$238
Mistral Large 3$0.50$1.50$0.0282$282
Qwen3.5 397B$0.60$3.60$0.0393$393
Kimi K2.6$0.95$4.00$0.0571$571
Claude Haiku 4.5$1.00$5.00$0.0625$625
Grok 4.3$1.25$2.50$0.0669$669
Qwen3.7 Max$1.25$3.75$0.0706$706
GLM-5.2$1.40$4.40$0.0797$797
Gemini 2.5 Pro$1.25$10.00$0.0894$894
Mistral Medium 3.5$1.50$7.50$0.0938$938
Gemini 3.5 Flash$1.50$9.00$0.0983$982
GPT-4.1$2.00$8.00$0.119$1,190
Claude Sonnet 5$2.00$10.00$0.125$1,250
GPT-4o$2.50$10.00$0.149$1,488
GPT-5.4$2.50$15.00$0.164$1,638
GPT-5.6 Terra$2.50$15.00$0.164$1,638
Claude Sonnet 4.6$3.00$15.00$0.188$1,875
Kimi K3$3.00$15.00$0.188$1,875
Claude Opus 4.8$5.00$25.00$0.312$3,125
GPT-5.5$5.00$30.00$0.328$3,275
GPT-5.6 Sol$5.00$30.00$0.328$3,275
Claude Fable 5$10.00$50.00$0.625$6,250

*At 10,000 outcomes per month. Cheapest model highlighted.

What this agent does

The clean-path steps this benchmark prices:

  1. Enrich (payee, history, device)
  2. Score Risk
  3. Low risk?

What drives the cost

This path runs 3 steps: 2 tool calls and 1 decision point. Tool steps make two model calls each, and the agent re-reads its growing context on every call. That compounding is why one Payment Fraud Detection outcome costs about 8.0x a single chat message ($0.188 on Claude Sonnet 4.6), not the price of one message.

Why these numbers matter.

How this benchmark is calculated

These figures are modeled estimates, not metered bills. We price a generic, representative Payment Fraud Detection workflow across 26 models using the same cost engine as the live estimator, at each model’s published list price (checked 26 Jul 2026), under documented default assumptions for planning loops, tool calls, memory retrieval, sub-agents and context size. Your own process will differ, so use these as starting points, tune the assumptions in the estimator, and validate against your real usage. Illustrative estimates, not financial advice.

Frequently asked questions

How much does an AI agent cost to run Payment Fraud Detection?

On the clean path with default assumptions, an agent for Payment Fraud Detection costs about $0.0089 to $0.625 per outcome depending on the model, or roughly $89 to $6,250 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0089; the most expensive is Claude Fable 5 at $0.625.

Why does an AI agent cost more than a single chatbot message?

An agent does not make one model call. It plans, calls tools, retrieves context and re-reads its growing working context on every step. For Payment Fraud Detection that adds up to about 8.0x the cost of a single chat message.

Which model is cheapest for Payment Fraud Detection?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0089 per outcome and Claude Fable 5 is the most expensive at $0.625. A cheaper model is not always the right choice, but it sets the floor for this workflow.

How can I reduce the cost of an agent for Payment Fraud Detection?

The biggest levers are prompt caching on the base context, fewer planning loops, smaller tool results, less retrieval, and choosing a cheaper model where quality allows. You can test each lever in the live estimator.

What is this Payment Fraud Detection benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Payment Fraud Detection workflow across 26 models with the same cost engine as the live estimator, at each model's published list price (checked 26 Jul 2026), under documented default assumptions for planning loops, tool calls, memory retrieval, sub-agents and context size. Your own process will differ, so treat these as starting points, tune them in the estimator, and validate against your own usage.

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