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

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

An agent for Incident Management on the clean path costs about $0.0203 to $2.03 per outcome depending on the model, around 26x the cost of a single chat message. At 10,000 outcomes a month that is roughly $203 to $20,270.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0203$203
GPT-6 Luna$0.10$0.50$0.0203$203
Llama 4 Maverick$0.25$0.87$0.0477$477
DeepSeek V4.1 Flash$0.30$1.20$0.0585$585
Gemini 2.5 Flash$0.30$2.50$0.0686$686
Mistral Large 3$0.50$1.50$0.0935$935
Gemini 3.8 Flash$0.75$3.75$0.152$1,520
Claude Haiku 4.5$1.00$5.00$0.203$2,027
DeepSeek V4 Pro$1.32$3.96$0.247$2,470
Mistral Large 4$1.36$4.18$0.255$2,552
Gemini 2.5 Pro$1.25$10.00$0.283$2,826
Grok 4.7$2.00$6.00$0.374$3,742
Claude Sonnet 5.5$2.00$10.00$0.405$4,054
Claude Sonnet 5$2.00$10.00$0.405$4,054
GPT-6.1 Sol$2.00$10.00$0.405$4,054
Gemini 4 Argon$2.00$10.00$0.405$4,054
Gemini 3.1 Pro$2.00$12.00$0.421$4,210
Claude Sonnet 4.6$3.00$15.00$0.608$6,081
Claude Opus 5.5$4.00$20.00$0.811$8,108
Claude Opus 5$5.00$25.00$1.01$10,135
Claude Opus 4.8$5.00$25.00$1.01$10,135
Claude Fable 5.1$10.00$50.00$2.03$20,270
Claude Fable 5$10.00$50.00$2.03$20,270
GPT-6 Astra$10.00$50.00$2.03$20,270

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

What this agent does

The clean-path steps this benchmark prices:

  1. Correlate & Dedupe
  2. Enrich (CMDB)
  3. Assess Severity
  4. Major incident?
  5. Diagnose & Plan
  6. Known error / runbook?
  7. Remediate
  8. Service restored?
  9. Verify with User
  10. Confirm & PIR

What drives the cost

This path runs 10 steps: 3 tool calls, 4 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 Incident Management outcome costs about 26x a single chat message ($0.608 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 Incident 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 Incident Management?

On the clean path with default assumptions, an agent for Incident Management costs about $0.0203 to $2.03 per outcome depending on the model, or roughly $203 to $20,270 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0203; the most expensive is GPT-6 Astra at $2.03.

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 Incident Management that adds up to about 26x the cost of a single chat message.

Which model is cheapest for Incident Management?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0203 per outcome and GPT-6 Astra is the most expensive at $2.03. 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 Incident 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 Incident Management benchmark based on?

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