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How much does an AI agent cost to run Change Enablement?

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

An agent for Change Enablement on the clean path costs about $0.0113 to $1.13 per outcome depending on the model, around 14x the cost of a single chat message. At 10,000 outcomes a month that is roughly $113 to $11,280.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0113$113
GPT-6 Luna$0.10$0.50$0.0113$113
Llama 4 Maverick$0.25$0.87$0.0261$261
DeepSeek V4.1 Flash$0.30$1.20$0.0322$322
Gemini 2.5 Flash$0.30$2.50$0.0392$392
Mistral Large 3$0.50$1.50$0.0510$510
Gemini 3.8 Flash$0.75$3.75$0.0846$846
Claude Haiku 4.5$1.00$5.00$0.113$1,128
DeepSeek V4 Pro$1.32$3.96$0.135$1,346
Mistral Large 4$1.36$4.18$0.139$1,393
Gemini 2.5 Pro$1.25$10.00$0.161$1,612
Grok 4.7$2.00$6.00$0.204$2,040
Claude Sonnet 5.5$2.00$10.00$0.226$2,256
Claude Sonnet 5$2.00$10.00$0.226$2,256
GPT-6.1 Sol$2.00$10.00$0.226$2,256
Gemini 4 Argon$2.00$10.00$0.226$2,256
Gemini 3.1 Pro$2.00$12.00$0.236$2,364
Claude Sonnet 4.6$3.00$15.00$0.338$3,384
Claude Opus 5.5$4.00$20.00$0.451$4,512
Claude Opus 5$5.00$25.00$0.564$5,640
Claude Opus 4.8$5.00$25.00$0.564$5,640
Claude Fable 5.1$10.00$50.00$1.13$11,280
Claude Fable 5$10.00$50.00$1.13$11,280
GPT-6 Astra$10.00$50.00$1.13$11,280

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

What this agent does

The clean-path steps this benchmark prices:

  1. Validate & Categorize
  2. Assess Impact & Plan
  3. Score Risk
  4. Pre-approved standard?
  5. Schedule Window
  6. Notify Stakeholders
  7. Implement Change
  8. Validation passed?
  9. PIR & Close

What drives the cost

This path runs 9 steps: 2 tool calls, 3 reasoning steps, 2 decision points and 2 automated system steps (no AI). Tool steps make two model calls each, and the agent re-reads its growing context on every call. That compounding is why one Change Enablement outcome costs about 14x a single chat message ($0.338 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 Change Enablement 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 Change Enablement?

On the clean path with default assumptions, an agent for Change Enablement costs about $0.0113 to $1.13 per outcome depending on the model, or roughly $113 to $11,280 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0113; the most expensive is GPT-6 Astra at $1.13.

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 Change Enablement that adds up to about 14x the cost of a single chat message.

Which model is cheapest for Change Enablement?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0113 per outcome and GPT-6 Astra is the most expensive at $1.13. 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 Change Enablement?

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 Change Enablement benchmark based on?

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