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How much does an AI agent cost to run HR Helpdesk?

Token cost benchmark for an autonomous HR Helpdesk agent, across 24 models. Prices as of 25 Oct 2026.

An agent for HR Helpdesk on the clean path costs about $0.0142 to $1.43 per outcome depending on the model, around 18x the cost of a single chat message. At 10,000 outcomes a month that is roughly $142 to $14,250.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0142$142
GPT-6 Luna$0.10$0.50$0.0142$142
Llama 4 Maverick$0.25$0.87$0.0333$333
DeepSeek V4.1 Flash$0.30$1.20$0.0410$410
Gemini 2.5 Flash$0.30$2.50$0.0488$488
Mistral Large 3$0.50$1.50$0.0653$652
Gemini 3.8 Flash$0.75$3.75$0.107$1,069
Claude Haiku 4.5$1.00$5.00$0.143$1,425
DeepSeek V4 Pro$1.32$3.96$0.172$1,723
Mistral Large 4$1.36$4.18$0.178$1,781
Gemini 2.5 Pro$1.25$10.00$0.201$2,006
Grok 4.7$2.00$6.00$0.261$2,610
Claude Sonnet 5.5$2.00$10.00$0.285$2,850
Claude Sonnet 5$2.00$10.00$0.285$2,850
GPT-6.1 Sol$2.00$10.00$0.285$2,850
Gemini 4 Argon$2.00$10.00$0.285$2,850
Gemini 3.1 Pro$2.00$12.00$0.297$2,970
Claude Sonnet 4.6$3.00$15.00$0.427$4,275
Claude Opus 5.5$4.00$20.00$0.570$5,700
Claude Opus 5$5.00$25.00$0.713$7,125
Claude Opus 4.8$5.00$25.00$0.713$7,125
Claude Fable 5.1$10.00$50.00$1.43$14,250
Claude Fable 5$10.00$50.00$1.43$14,250
GPT-6 Astra$10.00$50.00$1.43$14,250

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

What this agent does

The clean-path steps this benchmark prices:

  1. Classify Query
  2. Sensitive (ER/PII)?
  3. Search Policy & KB
  4. Answer found?
  5. Draft Response
  6. Confidence high?
  7. Send Response

What drives the cost

This path runs 7 steps: 3 tool calls, 1 reasoning step 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 HR Helpdesk outcome costs about 18x a single chat message ($0.427 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 HR Helpdesk workflow across 24 models using the same cost engine as the live estimator, at each model’s published list price (checked 25 Oct 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 HR Helpdesk?

On the clean path with default assumptions, an agent for HR Helpdesk costs about $0.0142 to $1.43 per outcome depending on the model, or roughly $142 to $14,250 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0142; the most expensive is GPT-6 Astra at $1.43.

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 HR Helpdesk that adds up to about 18x the cost of a single chat message.

Which model is cheapest for HR Helpdesk?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0142 per outcome and GPT-6 Astra is the most expensive at $1.43. 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 HR Helpdesk?

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 HR Helpdesk benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative HR Helpdesk workflow across 24 models with the same cost engine as the live estimator, at each model's published list price (checked 25 Oct 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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