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How much does an AI agent cost to run Campaign Ops?

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

An agent for Campaign Ops on the clean path costs about $0.0168 to $1.68 per outcome depending on the model, around 22x the cost of a single chat message. At 10,000 outcomes a month that is roughly $168 to $16,780.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0168$168
GPT-6 Luna$0.10$0.50$0.0168$168
Llama 4 Maverick$0.25$0.87$0.0394$394
DeepSeek V4.1 Flash$0.30$1.20$0.0484$484
Gemini 2.5 Flash$0.30$2.50$0.0569$569
Mistral Large 3$0.50$1.50$0.0773$773
Gemini 3.8 Flash$0.75$3.75$0.126$1,258
Claude Haiku 4.5$1.00$5.00$0.168$1,678
DeepSeek V4 Pro$1.32$3.96$0.204$2,041
Mistral Large 4$1.36$4.18$0.211$2,109
Gemini 2.5 Pro$1.25$10.00$0.235$2,345
Grok 4.7$2.00$6.00$0.309$3,092
Claude Sonnet 5.5$2.00$10.00$0.336$3,356
Claude Sonnet 5$2.00$10.00$0.336$3,356
GPT-6.1 Sol$2.00$10.00$0.336$3,356
Gemini 4 Argon$2.00$10.00$0.336$3,356
Gemini 3.1 Pro$2.00$12.00$0.349$3,488
Claude Sonnet 4.6$3.00$15.00$0.503$5,034
Claude Opus 5.5$4.00$20.00$0.671$6,712
Claude Opus 5$5.00$25.00$0.839$8,390
Claude Opus 4.8$5.00$25.00$0.839$8,390
Claude Fable 5.1$10.00$50.00$1.68$16,780
Claude Fable 5$10.00$50.00$1.68$16,780
GPT-6 Astra$10.00$50.00$1.68$16,780

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

What this agent does

The clean-path steps this benchmark prices:

  1. Parse Brief
  2. Brief complete?
  3. Assemble Assets
  4. QA & Compliance Scan
  5. Risk flags?
  6. Confidence high?
  7. Schedule & Launch
  8. Set Up Tracking

What drives the cost

This path runs 8 steps: 3 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 Campaign Ops outcome costs about 22x a single chat message ($0.503 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 Campaign Ops 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 Campaign Ops?

On the clean path with default assumptions, an agent for Campaign Ops costs about $0.0168 to $1.68 per outcome depending on the model, or roughly $168 to $16,780 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0168; the most expensive is GPT-6 Astra at $1.68.

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 Campaign Ops that adds up to about 22x the cost of a single chat message.

Which model is cheapest for Campaign Ops?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0168 per outcome and GPT-6 Astra is the most expensive at $1.68. 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 Campaign Ops?

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 Campaign Ops benchmark based on?

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