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

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

An agent for Problem Management on the clean path costs about $0.0135 to $1.35 per outcome depending on the model, around 17x the cost of a single chat message. At 10,000 outcomes a month that is roughly $135 to $13,500.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0135$135
GPT-6 Luna$0.10$0.50$0.0135$135
Llama 4 Maverick$0.25$0.87$0.0315$315
DeepSeek V4.1 Flash$0.30$1.20$0.0387$387
Gemini 2.5 Flash$0.30$2.50$0.0465$465
Mistral Large 3$0.50$1.50$0.0615$615
Gemini 3.8 Flash$0.75$3.75$0.101$1,012
Claude Haiku 4.5$1.00$5.00$0.135$1,350
DeepSeek V4 Pro$1.32$3.96$0.162$1,624
Mistral Large 4$1.36$4.18$0.168$1,679
Gemini 2.5 Pro$1.25$10.00$0.191$1,912
Grok 4.7$2.00$6.00$0.246$2,460
Claude Sonnet 5.5$2.00$10.00$0.270$2,700
Claude Sonnet 5$2.00$10.00$0.270$2,700
GPT-6.1 Sol$2.00$10.00$0.270$2,700
Gemini 4 Argon$2.00$10.00$0.270$2,700
Gemini 3.1 Pro$2.00$12.00$0.282$2,820
Claude Sonnet 4.6$3.00$15.00$0.405$4,050
Claude Opus 5.5$4.00$20.00$0.540$5,400
Claude Opus 5$5.00$25.00$0.675$6,750
Claude Opus 4.8$5.00$25.00$0.675$6,750
Claude Fable 5.1$10.00$50.00$1.35$13,500
Claude Fable 5$10.00$50.00$1.35$13,500
GPT-6 Astra$10.00$50.00$1.35$13,500

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

What this agent does

The clean-path steps this benchmark prices:

  1. Identify Problem
  2. Genuine problem?
  3. Root-Cause Analysis
  4. Root cause found?
  5. Fix available?
  6. Needs change?
  7. Confidence high?
  8. Implement & Close

What drives the cost

This path runs 8 steps: 2 tool calls, 1 reasoning step and 5 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 Problem Management outcome costs about 17x a single chat message ($0.405 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 Problem 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 Problem Management?

On the clean path with default assumptions, an agent for Problem Management costs about $0.0135 to $1.35 per outcome depending on the model, or roughly $135 to $13,500 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0135; the most expensive is GPT-6 Astra at $1.35.

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 Problem Management that adds up to about 17x the cost of a single chat message.

Which model is cheapest for Problem Management?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0135 per outcome and GPT-6 Astra is the most expensive at $1.35. 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 Problem 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 Problem Management benchmark based on?

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