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

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

An agent for Patient Intake on the clean path costs about $0.0172 to $1.20 per outcome depending on the model, around 15x the cost of a single chat message. At 10,000 outcomes a month that is roughly $172 to $12,030.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0172$172
Llama 4 Maverick$0.27$0.85$0.0298$298
Gemini 2.5 Flash$0.30$2.50$0.0415$415
DeepSeek V4$0.43$0.87$0.0453$453
GPT-4.1 mini$0.40$1.60$0.0460$460
Mistral Large 3$0.50$1.50$0.0547$547
Qwen3.5 397B$0.60$3.60$0.0754$754
Kimi K2.6$0.95$4.00$0.110$1,102
Claude Haiku 4.5$1.00$5.00$0.120$1,203
Grok 4.3$1.25$2.50$0.130$1,301
Qwen3.7 Max$1.25$3.75$0.137$1,369
GLM-5.2$1.40$4.40$0.154$1,544
Gemini 2.5 Pro$1.25$10.00$0.171$1,706
Mistral Medium 3.5$1.50$7.50$0.180$1,804
Gemini 3.5 Flash$1.50$9.00$0.189$1,886
GPT-4.1$2.00$8.00$0.230$2,298
Claude Sonnet 5$2.00$10.00$0.241$2,406
GPT-4o$2.50$10.00$0.287$2,872
GPT-5.4$2.50$15.00$0.314$3,143
GPT-5.6 Terra$2.50$15.00$0.314$3,143
Claude Sonnet 4.6$3.00$15.00$0.361$3,609
Kimi K3$3.00$15.00$0.361$3,609
Claude Opus 4.8$5.00$25.00$0.602$6,015
GPT-5.5$5.00$30.00$0.629$6,285
GPT-5.6 Sol$5.00$30.00$0.629$6,285
Claude Fable 5$10.00$50.00$1.20$12,030

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

What this agent does

The clean-path steps this benchmark prices:

  1. Collect Forms
  2. Forms complete?
  3. Verify ID & Insurance
  4. Coverage active?
  5. Data confident?
  6. Create / Update Record
  7. Generate Intake Summary

What drives the cost

This path runs 7 steps: 2 tool calls, 2 reasoning steps and 3 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 Patient Intake outcome costs about 15x a single chat message ($0.361 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 Patient 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 Patient Intake?

On the clean path with default assumptions, an agent for Patient Intake costs about $0.0172 to $1.20 per outcome depending on the model, or roughly $172 to $12,030 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0172; the most expensive is Claude Fable 5 at $1.20.

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 Patient Intake that adds up to about 15x the cost of a single chat message.

Which model is cheapest for Patient Intake?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0172 per outcome and Claude Fable 5 is the most expensive at $1.20. 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 Patient 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 Patient Intake benchmark based on?

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