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Software Engineering / DevOps

How much does an AI agent cost to run Test-Failure Triage?

Token cost benchmark for an autonomous Test-Failure Triage agent, across 24 models. Prices as of 25 Oct 2026.

An agent for Test-Failure Triage on the clean path costs about $0.0191 to $1.91 per outcome depending on the model, around 25x the cost of a single chat message. At 10,000 outcomes a month that is roughly $191 to $19,120.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0191$191
GPT-6 Luna$0.10$0.50$0.0191$191
Llama 4 Maverick$0.25$0.87$0.0451$451
DeepSeek V4.1 Flash$0.30$1.20$0.0552$552
Gemini 2.5 Flash$0.30$2.50$0.0646$646
Mistral Large 3$0.50$1.50$0.0884$884
Gemini 3.8 Flash$0.75$3.75$0.143$1,434
Claude Haiku 4.5$1.00$5.00$0.191$1,912
DeepSeek V4 Pro$1.32$3.96$0.233$2,334
Mistral Large 4$1.36$4.18$0.241$2,412
Gemini 2.5 Pro$1.25$10.00$0.266$2,660
Grok 4.7$2.00$6.00$0.354$3,536
Claude Sonnet 5.5$2.00$10.00$0.382$3,824
Claude Sonnet 5$2.00$10.00$0.382$3,824
GPT-6.1 Sol$2.00$10.00$0.382$3,824
Gemini 4 Argon$2.00$10.00$0.382$3,824
Gemini 3.1 Pro$2.00$12.00$0.397$3,968
Claude Sonnet 4.6$3.00$15.00$0.574$5,736
Claude Opus 5.5$4.00$20.00$0.765$7,648
Claude Opus 5$5.00$25.00$0.956$9,560
Claude Opus 4.8$5.00$25.00$0.956$9,560
Claude Fable 5.1$10.00$50.00$1.91$19,120
Claude Fable 5$10.00$50.00$1.91$19,120
GPT-6 Astra$10.00$50.00$1.91$19,120

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

What this agent does

The clean-path steps this benchmark prices:

  1. Collect Logs & Diffs
  2. Infra / env failure?
  3. Classify Failure
  4. Flaky test?
  5. Bisect & Blame
  6. Culprit found?
  7. Auto- fixable?
  8. Draft Fix PR
  9. Confidence high?

What drives the cost

This path runs 9 steps: 3 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 Test-Failure Triage outcome costs about 25x a single chat message ($0.574 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 Test-Failure Triage 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 Test-Failure Triage?

On the clean path with default assumptions, an agent for Test-Failure Triage costs about $0.0191 to $1.91 per outcome depending on the model, or roughly $191 to $19,120 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0191; the most expensive is GPT-6 Astra at $1.91.

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 Test-Failure Triage that adds up to about 25x the cost of a single chat message.

Which model is cheapest for Test-Failure Triage?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0191 per outcome and GPT-6 Astra is the most expensive at $1.91. 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 Test-Failure Triage?

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 Test-Failure Triage benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Test-Failure Triage 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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