Claude vs GPT-5 for Automated Code Review: Which Should You Choose?
Cody now supports both Anthropic's Claude and OpenAI's GPT-5 family for automated pull request reviews. So which model should be reviewing your code? Here's a practical comparison — and since Cody lets you switch with a single environment variable, you can easily try both on your own pull requests.
The short answer
- Want the strongest reviews? Try
claude-opus-4-8— Anthropic's most capable Opus-tier model, particularly strong at spotting real bugs and explaining them clearly. - Want great reviews at a lower price?
claude-sonnet-5delivers near-Opus quality on coding tasks. - High-volume repos on a budget?
claude-haiku-4-5is the fastest, cheapest Claude option and still handles routine reviews well. - Already invested in OpenAI?
gpt-5and the coding-focusedgpt-5-codexremain fully supported — nothing changes for existing Cody users.
What matters in a code review model
Finding real bugs, not nitpicks. The best review models flag functional and security issues and skip style bikeshedding. Cody's prompt explicitly asks for that focus regardless of which model you choose, but more capable models are noticeably better at separating signal from noise.
Reading large diffs in context. Claude's current models accept up to 1 million tokens of context, so even very large pull requests fit comfortably in a single review.
Cost per review. As of July 2026, Anthropic's list pricing per million tokens is roughly: Claude Opus 4.8 at $5 input / $25 output, Claude Sonnet 5 at $3 / $15 (with introductory pricing of $2 / $10 until the end of August 2026), and Claude Haiku 4.5 at $1 / $5. A typical pull request review uses a small fraction of a million tokens, so per-review costs are usually cents. OpenAI pricing varies by model — check their pricing page for current rates.
Try both in one afternoon
Switching models in Cody is a one-line change:
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
MODEL: "claude-opus-4-8" # or "claude-sonnet-5", "gpt-5", "gpt-5-codex"...
Run a few pull requests with each and compare the reviews on your own codebase — that beats any benchmark table.
- Setup Guide: Quick Start Documentation
- GitHub: https://github.com/codylabs/cody-code-reviewer
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