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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 26 models. Prices as of 26 Jul 2026.

An agent for Code/PR Review on the clean path costs about $0.0334 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 $334 to $23,020.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0334$334
Llama 4 Maverick$0.27$0.85$0.0583$583
Gemini 2.5 Flash$0.30$2.50$0.0769$769
GPT-4.1 mini$0.40$1.60$0.0890$890
DeepSeek V4$0.43$0.87$0.0900$900
Mistral Large 3$0.50$1.50$0.107$1,073
Qwen3.5 397B$0.60$3.60$0.143$1,428
Kimi K2.6$0.95$4.00$0.213$2,128
Claude Haiku 4.5$1.00$5.00$0.230$2,302
Grok 4.3$1.25$2.50$0.259$2,585
Qwen3.7 Max$1.25$3.75$0.268$2,682
GLM-5.2$1.40$4.40$0.302$3,020
Gemini 2.5 Pro$1.25$10.00$0.317$3,170
Mistral Medium 3.5$1.50$7.50$0.345$3,453
Gemini 3.5 Flash$1.50$9.00$0.357$3,570
GPT-4.1$2.00$8.00$0.445$4,448
Claude Sonnet 5$2.00$10.00$0.460$4,604
GPT-4o$2.50$10.00$0.556$5,560
GPT-5.4$2.50$15.00$0.595$5,950
GPT-5.6 Terra$2.50$15.00$0.595$5,950
Claude Sonnet 4.6$3.00$15.00$0.691$6,906
Kimi K3$3.00$15.00$0.691$6,906
Claude Opus 4.8$5.00$25.00$1.15$11,510
GPT-5.5$5.00$30.00$1.19$11,900
GPT-5.6 Sol$5.00$30.00$1.19$11,900
Claude Fable 5$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 26 models using the same cost engine as the live estimator, at each model’s published list price (checked 26 Jul 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 $0.0334 to $2.30 per outcome depending on the model, or roughly $334 to $23,020 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0334; the most expensive is Claude Fable 5 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 26 models benchmarked, GPT-4o mini is cheapest at $0.0334 per outcome and Claude Fable 5 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 26 models with the same cost engine as the live estimator, at each model's published list price (checked 26 Jul 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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