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Software Engineering / DevOps

How much does an AI agent cost to run Build & Release?

Token cost benchmark for an autonomous Build & Release agent, across 24 models. Prices as of 25 Oct 2026.

An agent for Build & Release on the clean path costs about $0.0204 to $2.04 per outcome depending on the model, around 26x the cost of a single chat message. At 10,000 outcomes a month that is roughly $204 to $20,370.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0204$204
GPT-6 Luna$0.10$0.50$0.0204$204
Llama 4 Maverick$0.25$0.87$0.0482$482
DeepSeek V4.1 Flash$0.30$1.20$0.0589$590
Gemini 2.5 Flash$0.30$2.50$0.0683$683
Mistral Large 3$0.50$1.50$0.0946$946
Gemini 3.8 Flash$0.75$3.75$0.153$1,528
Claude Haiku 4.5$1.00$5.00$0.204$2,037
DeepSeek V4 Pro$1.32$3.96$0.250$2,499
Mistral Large 4$1.36$4.18$0.258$2,582
Gemini 2.5 Pro$1.25$10.00$0.282$2,816
Grok 4.7$2.00$6.00$0.379$3,786
Claude Sonnet 5.5$2.00$10.00$0.407$4,074
Claude Sonnet 5$2.00$10.00$0.407$4,074
GPT-6.1 Sol$2.00$10.00$0.407$4,074
Gemini 4 Argon$2.00$10.00$0.407$4,074
Gemini 3.1 Pro$2.00$12.00$0.422$4,218
Claude Sonnet 4.6$3.00$15.00$0.611$6,111
Claude Opus 5.5$4.00$20.00$0.815$8,148
Claude Opus 5$5.00$25.00$1.02$10,185
Claude Opus 4.8$5.00$25.00$1.02$10,185
Claude Fable 5.1$10.00$50.00$2.04$20,370
Claude Fable 5$10.00$50.00$2.04$20,370
GPT-6 Astra$10.00$50.00$2.04$20,370

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

What this agent does

The clean-path steps this benchmark prices:

  1. Build & Package
  2. Build OK?
  3. Run Release Gates
  4. Gates pass?
  5. Production deploy?
  6. Deploy (canary)
  7. Smoke & Health Check
  8. Healthy?

What drives the cost

This path runs 8 steps: 4 tool calls and 4 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 Build & Release outcome costs about 26x a single chat message ($0.611 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 Build & Release 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 Build & Release?

On the clean path with default assumptions, an agent for Build & Release costs about $0.0204 to $2.04 per outcome depending on the model, or roughly $204 to $20,370 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0204; the most expensive is GPT-6 Astra at $2.04.

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 Build & Release that adds up to about 26x the cost of a single chat message.

Which model is cheapest for Build & Release?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0204 per outcome and GPT-6 Astra is the most expensive at $2.04. 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 Build & Release?

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 Build & Release benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Build & Release 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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