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How much does an AI agent cost to run FNOL Intake?

Token cost benchmark for an autonomous FNOL Intake agent, across 26 models. Prices as of 26 Jul 2026.

An agent for FNOL Intake on the clean path costs about $0.0224 to $1.55 per outcome depending on the model, around 20x the cost of a single chat message. At 10,000 outcomes a month that is roughly $224 to $15,500.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0224$224
Llama 4 Maverick$0.27$0.85$0.0389$388
Gemini 2.5 Flash$0.30$2.50$0.0525$525
DeepSeek V4$0.43$0.87$0.0596$596
GPT-4.1 mini$0.40$1.60$0.0596$596
Mistral Large 3$0.50$1.50$0.0715$715
Qwen3.5 397B$0.60$3.60$0.0966$966
Kimi K2.6$0.95$4.00$0.143$1,427
Claude Haiku 4.5$1.00$5.00$0.155$1,550
Grok 4.3$1.25$2.50$0.171$1,713
Qwen3.7 Max$1.25$3.75$0.179$1,788
GLM-5.2$1.40$4.40$0.201$2,014
Gemini 2.5 Pro$1.25$10.00$0.216$2,163
Mistral Medium 3.5$1.50$7.50$0.233$2,325
Gemini 3.5 Flash$1.50$9.00$0.241$2,415
GPT-4.1$2.00$8.00$0.298$2,980
Claude Sonnet 5$2.00$10.00$0.310$3,100
GPT-4o$2.50$10.00$0.373$3,725
GPT-5.4$2.50$15.00$0.403$4,025
GPT-5.6 Terra$2.50$15.00$0.403$4,025
Claude Sonnet 4.6$3.00$15.00$0.465$4,650
Kimi K3$3.00$15.00$0.465$4,650
Claude Opus 4.8$5.00$25.00$0.775$7,750
GPT-5.5$5.00$30.00$0.805$8,050
GPT-5.6 Sol$5.00$30.00$0.805$8,050
Claude Fable 5$10.00$50.00$1.55$15,500

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

What this agent does

The clean-path steps this benchmark prices:

  1. Capture Loss Details
  2. Verify Policy & Coverage
  3. Covered?
  4. Details complete?
  5. High severity / complex?
  6. Confidence high?
  7. Create & Assign Claim

What drives the cost

This path runs 7 steps: 3 tool calls and 4 decision points. Tool steps make two model calls each, and the agent re-reads its growing context on every call. That compounding is why one FNOL Intake outcome costs about 20x a single chat message ($0.465 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 FNOL Intake 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 FNOL Intake?

On the clean path with default assumptions, an agent for FNOL Intake costs about $0.0224 to $1.55 per outcome depending on the model, or roughly $224 to $15,500 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0224; the most expensive is Claude Fable 5 at $1.55.

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 FNOL Intake that adds up to about 20x the cost of a single chat message.

Which model is cheapest for FNOL Intake?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0224 per outcome and Claude Fable 5 is the most expensive at $1.55. 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 FNOL Intake?

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 FNOL Intake benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative FNOL Intake 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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