An AI code reviewer successfully cut its average review cost by 68%, bringing it down to $0.77 from a peak of $2.43. This optimization effort also decreased the median review time by 63%, from 4 minutes 09 seconds to 1 minute 32 seconds.
Approximately 40% of the cost savings resulted from changes and optimizations within the reviewer's code. The remaining savings came from experimenting with different AI models and adjusting their effort levels during reviews. These efforts were initiated in mid-September to aggressively reduce token usage.
The average review cost had steadily risen from $1.19 in April to $2.46 by the end of August. This increase was primarily attributed to model changes, such as moving to Anthropic models in August, which raised the cost per job from $1.38 to $2.03. The internal standards reviewer, Codex, also saw its cost increase from $0.25 to $0.88 per review after being moved to Claude Opus 4.7.
Additionally, the number of tokens per review more than tripled from 0.91 million to 2.99 million, contributing to the higher costs despite an increased cache hit rate from 85.7% to 97%.
A small portion of the cost increase was due to more reviews falling into the most expensive tier, rising from 59% in March to 65% in August. This is believed to be a consequence of AI models becoming more capable, leading engineers to produce larger and more complex code changes that require more intensive reviews.
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An AI code reviewer reduced its average review cost by 68% to $0.77 and median review time by 63% to 1 minute 32 seconds. This optimization was achieved through code changes, model selection, and managing token usage, addressing a previous cost increase from $1.19 to $2.46 per review.