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Software Engineering / DevOps

How much does an AI agent cost to run Code/PR Review?

Token cost benchmark for an autonomous Code/PR Review agent, across 24 models. Prices as of 25 Oct 2026.

An agent for Code/PR Review on the clean path costs about $0.0230 to $2.30 per outcome depending on the model, around 30x the cost of a single chat message. At 10,000 outcomes a month that is roughly $230 to $23,020.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0230$230
GPT-6 Luna$0.10$0.50$0.0230$230
Llama 4 Maverick$0.25$0.87$0.0546$546
DeepSeek V4.1 Flash$0.30$1.20$0.0667$667
Gemini 2.5 Flash$0.30$2.50$0.0769$769
Mistral Large 3$0.50$1.50$0.107$1,073
Gemini 3.8 Flash$0.75$3.75$0.173$1,726
Claude Haiku 4.5$1.00$5.00$0.230$2,302
DeepSeek V4 Pro$1.32$3.96$0.283$2,833
Mistral Large 4$1.36$4.18$0.293$2,926
Gemini 2.5 Pro$1.25$10.00$0.317$3,170
Grok 4.7$2.00$6.00$0.429$4,292
Claude Sonnet 5.5$2.00$10.00$0.460$4,604
Claude Sonnet 5$2.00$10.00$0.460$4,604
GPT-6.1 Sol$2.00$10.00$0.460$4,604
Gemini 4 Argon$2.00$10.00$0.460$4,604
Gemini 3.1 Pro$2.00$12.00$0.476$4,760
Claude Sonnet 4.6$3.00$15.00$0.691$6,906
Claude Opus 5.5$4.00$20.00$0.921$9,208
Claude Opus 5$5.00$25.00$1.15$11,510
Claude Opus 4.8$5.00$25.00$1.15$11,510
Claude Fable 5.1$10.00$50.00$2.30$23,020
Claude Fable 5$10.00$50.00$2.30$23,020
GPT-6 Astra$10.00$50.00$2.30$23,020

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

What this agent does

The clean-path steps this benchmark prices:

  1. Fetch Diff & Context
  2. Sane size & scope?
  3. Run Lint & SAST
  4. Run Tests & Coverage
  5. Checks pass?
  6. Assess Quality & Risk
  7. High-risk change?
  8. Confidence high?
  9. Approve & Comment

What drives the cost

This path runs 9 steps: 4 tool calls, 1 reasoning step 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 Code/PR Review outcome costs about 30x a single chat message ($0.691 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 Code/PR Review 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 Code/PR Review?

On the clean path with default assumptions, an agent for Code/PR Review costs about $0.0230 to $2.30 per outcome depending on the model, or roughly $230 to $23,020 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0230; the most expensive is GPT-6 Astra at $2.30.

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 Code/PR Review that adds up to about 30x the cost of a single chat message.

Which model is cheapest for Code/PR Review?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0230 per outcome and GPT-6 Astra is the most expensive at $2.30. 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 Code/PR Review?

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 Code/PR Review benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Code/PR Review 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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