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How much does an AI agent cost to run Exception Handling?

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

An agent for Exception Handling on the clean path costs about $0.0080 to $0.802 per outcome depending on the model, around 10x the cost of a single chat message. At 10,000 outcomes a month that is roughly $80 to $8,020.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0080$80
GPT-6 Luna$0.10$0.50$0.0080$80
Llama 4 Maverick$0.25$0.87$0.0185$185
DeepSeek V4.1 Flash$0.30$1.20$0.0228$228
Gemini 2.5 Flash$0.30$2.50$0.0283$283
Mistral Large 3$0.50$1.50$0.0359$359
Gemini 3.8 Flash$0.75$3.75$0.0601$602
Claude Haiku 4.5$1.00$5.00$0.0802$802
DeepSeek V4 Pro$1.32$3.96$0.0948$948
Mistral Large 4$1.36$4.18$0.0981$981
Gemini 2.5 Pro$1.25$10.00$0.116$1,160
Grok 4.7$2.00$6.00$0.144$1,436
Claude Sonnet 5.5$2.00$10.00$0.160$1,604
Claude Sonnet 5$2.00$10.00$0.160$1,604
GPT-6.1 Sol$2.00$10.00$0.160$1,604
Gemini 4 Argon$2.00$10.00$0.160$1,604
Gemini 3.1 Pro$2.00$12.00$0.169$1,688
Claude Sonnet 4.6$3.00$15.00$0.241$2,406
Claude Opus 5.5$4.00$20.00$0.321$3,208
Claude Opus 5$5.00$25.00$0.401$4,010
Claude Opus 4.8$5.00$25.00$0.401$4,010
Claude Fable 5.1$10.00$50.00$0.802$8,020
Claude Fable 5$10.00$50.00$0.802$8,020
GPT-6 Astra$10.00$50.00$0.802$8,020

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

What this agent does

The clean-path steps this benchmark prices:

  1. Classify Exception
  2. Known pattern?
  3. Severe / customer-hit?
  4. Auto- remediable?
  5. Apply Remediation
  6. Resolved?
  7. Close & Log

What drives the cost

This path runs 7 steps: 1 tool call, 1 reasoning step, 4 decision points and 1 automated system step (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 Exception Handling outcome costs about 10x a single chat message ($0.241 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 Exception Handling 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 Exception Handling?

On the clean path with default assumptions, an agent for Exception Handling costs about $0.0080 to $0.802 per outcome depending on the model, or roughly $80 to $8,020 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0080; the most expensive is GPT-6 Astra at $0.802.

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 Exception Handling that adds up to about 10x the cost of a single chat message.

Which model is cheapest for Exception Handling?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0080 per outcome and GPT-6 Astra is the most expensive at $0.802. 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 Exception Handling?

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 Exception Handling benchmark based on?

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