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How much does an AI agent cost to run Asset / Config Management?

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

An agent for Asset / Config Management on the clean path costs about $0.0155 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 $155 to $15,500.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0155$155
GPT-6 Luna$0.10$0.50$0.0155$155
Llama 4 Maverick$0.25$0.87$0.0365$365
DeepSeek V4.1 Flash$0.30$1.20$0.0447$447
Gemini 2.5 Flash$0.30$2.50$0.0525$525
Mistral Large 3$0.50$1.50$0.0715$715
Gemini 3.8 Flash$0.75$3.75$0.116$1,162
Claude Haiku 4.5$1.00$5.00$0.155$1,550
DeepSeek V4 Pro$1.32$3.96$0.189$1,888
Mistral Large 4$1.36$4.18$0.195$1,951
Gemini 2.5 Pro$1.25$10.00$0.216$2,163
Grok 4.7$2.00$6.00$0.286$2,860
Claude Sonnet 5.5$2.00$10.00$0.310$3,100
Claude Sonnet 5$2.00$10.00$0.310$3,100
GPT-6.1 Sol$2.00$10.00$0.310$3,100
Gemini 4 Argon$2.00$10.00$0.310$3,100
Gemini 3.1 Pro$2.00$12.00$0.322$3,220
Claude Sonnet 4.6$3.00$15.00$0.465$4,650
Claude Opus 5.5$4.00$20.00$0.620$6,200
Claude Opus 5$5.00$25.00$0.775$7,750
Claude Opus 4.8$5.00$25.00$0.775$7,750
Claude Fable 5.1$10.00$50.00$1.55$15,500
Claude Fable 5$10.00$50.00$1.55$15,500
GPT-6 Astra$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. Scan Estate
  2. Reconcile CMDB
  3. Discrep- ancies?
  4. Classify Drift
  5. License / EOL issue?
  6. Confidence high?
  7. Update CMDB

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 Asset / Config Management 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 Asset / Config 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 Asset / Config Management?

On the clean path with default assumptions, an agent for Asset / Config Management costs about $0.0155 to $1.55 per outcome depending on the model, or roughly $155 to $15,500 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0155; the most expensive is GPT-6 Astra 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 Asset / Config Management that adds up to about 20x the cost of a single chat message.

Which model is cheapest for Asset / Config Management?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0155 per outcome and GPT-6 Astra 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 Asset / Config 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 Asset / Config Management benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Asset / Config 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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