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How much does an AI agent cost to run Expense Management?

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

An agent for Expense Management on the clean path costs about $0.0152 to $1.53 per outcome depending on the model, around 20x the cost of a single chat message. At 10,000 outcomes a month that is roughly $152 to $15,250.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0152$152
GPT-6 Luna$0.10$0.50$0.0152$152
Llama 4 Maverick$0.25$0.87$0.0358$358
DeepSeek V4.1 Flash$0.30$1.20$0.0439$439
Gemini 2.5 Flash$0.30$2.50$0.0518$518
Mistral Large 3$0.50$1.50$0.0702$702
Gemini 3.8 Flash$0.75$3.75$0.114$1,144
Claude Haiku 4.5$1.00$5.00$0.153$1,525
DeepSeek V4 Pro$1.32$3.96$0.185$1,855
Mistral Large 4$1.36$4.18$0.192$1,917
Gemini 2.5 Pro$1.25$10.00$0.213$2,131
Grok 4.7$2.00$6.00$0.281$2,810
Claude Sonnet 5.5$2.00$10.00$0.305$3,050
Claude Sonnet 5$2.00$10.00$0.305$3,050
GPT-6.1 Sol$2.00$10.00$0.305$3,050
Gemini 4 Argon$2.00$10.00$0.305$3,050
Gemini 3.1 Pro$2.00$12.00$0.317$3,170
Claude Sonnet 4.6$3.00$15.00$0.457$4,575
Claude Opus 5.5$4.00$20.00$0.610$6,100
Claude Opus 5$5.00$25.00$0.763$7,625
Claude Opus 4.8$5.00$25.00$0.763$7,625
Claude Fable 5.1$10.00$50.00$1.53$15,250
Claude Fable 5$10.00$50.00$1.53$15,250
GPT-6 Astra$10.00$50.00$1.53$15,250

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

What this agent does

The clean-path steps this benchmark prices:

  1. Extract & Itemise
  2. Receipts complete?
  3. Check Policy
  4. Policy violation?
  5. Amount material?
  6. Confidence high?
  7. Approve & Reimburse

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 Expense Management outcome costs about 20x a single chat message ($0.457 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 Expense Management 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 Expense Management?

On the clean path with default assumptions, an agent for Expense Management costs about $0.0152 to $1.53 per outcome depending on the model, or roughly $152 to $15,250 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0152; the most expensive is GPT-6 Astra at $1.53.

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 Expense Management that adds up to about 20x the cost of a single chat message.

Which model is cheapest for Expense Management?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0152 per outcome and GPT-6 Astra is the most expensive at $1.53. 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 Expense Management?

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 Expense Management benchmark based on?

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