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How much does an AI agent cost to run Loan Origination?

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

An agent for Loan Origination on the clean path costs about $0.0249 to $1.73 per outcome depending on the model, around 22x the cost of a single chat message. At 10,000 outcomes a month that is roughly $249 to $17,280.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0249$249
Llama 4 Maverick$0.27$0.85$0.0434$434
Gemini 2.5 Flash$0.30$2.50$0.0584$584
GPT-4.1 mini$0.40$1.60$0.0665$665
DeepSeek V4$0.43$0.87$0.0666$666
Mistral Large 3$0.50$1.50$0.0798$798
Qwen3.5 397B$0.60$3.60$0.108$1,076
Kimi K2.6$0.95$4.00$0.159$1,592
Claude Haiku 4.5$1.00$5.00$0.173$1,728
Grok 4.3$1.25$2.50$0.191$1,913
Qwen3.7 Max$1.25$3.75$0.200$1,995
GLM-5.2$1.40$4.40$0.225$2,248
Gemini 2.5 Pro$1.25$10.00$0.241$2,408
Mistral Medium 3.5$1.50$7.50$0.259$2,592
Gemini 3.5 Flash$1.50$9.00$0.269$2,691
GPT-4.1$2.00$8.00$0.332$3,324
Claude Sonnet 5$2.00$10.00$0.346$3,456
GPT-4o$2.50$10.00$0.416$4,155
GPT-5.4$2.50$15.00$0.449$4,485
GPT-5.6 Terra$2.50$15.00$0.449$4,485
Claude Sonnet 4.6$3.00$15.00$0.518$5,184
Kimi K3$3.00$15.00$0.518$5,184
Claude Opus 4.8$5.00$25.00$0.864$8,640
GPT-5.5$5.00$30.00$0.897$8,970
GPT-5.6 Sol$5.00$30.00$0.897$8,970
Claude Fable 5$10.00$50.00$1.73$17,280

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

What this agent does

The clean-path steps this benchmark prices:

  1. Pull Credit & Income
  2. Data complete?
  3. Underwrite & Score
  4. Meets criteria?
  5. Auto- decision?
  6. Conditions met?
  7. Confidence high?
  8. Approve & Fund

What drives the cost

This path runs 8 steps: 3 tool calls and 5 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 Loan Origination outcome costs about 22x a single chat message ($0.518 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 Loan Origination 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 Loan Origination?

On the clean path with default assumptions, an agent for Loan Origination costs about $0.0249 to $1.73 per outcome depending on the model, or roughly $249 to $17,280 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0249; the most expensive is Claude Fable 5 at $1.73.

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 Loan Origination that adds up to about 22x the cost of a single chat message.

Which model is cheapest for Loan Origination?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0249 per outcome and Claude Fable 5 is the most expensive at $1.73. 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 Loan Origination?

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 Loan Origination benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Loan Origination 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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Beyond cost: is it ready, and can you govern it?

Cost is one axis. Before you build, check the process is ready for AI and that you can prove you govern it. See how governed AI process animations work →

Open Loan Origination in the live estimator →