Developer workflow
GitHub Code Analysis Cost
Code repositories often have high token density because source code, Markdown, configs, tests, and scripts are mostly text.
Start with the right files
Repository size can be misleading. A 500 MB repository might include dependencies, compiled assets, screenshots, datasets, or build output that should not be sent to a language model. A smaller 25 MB source-only repository can still contain millions of tokens because code is dense text.
Before estimating cost, exclude dependency folders, build output, minified bundles, generated files, logs, cache directories, large lockfiles, and binary assets unless they are directly relevant to the review. This improves both cost and answer quality.
Count analysis rounds
Code analysis is rarely a single request. A realistic workflow may include repository mapping, architecture review, bug finding, security review, refactor planning, patch generation, and final verification. Each round may send overlapping context, so workflow rounds matter.
Example
A filtered JavaScript repository has 8 million estimated input tokens. If an architecture review uses 35% output, one full pass is about 10.8 million tokens. Five separate review rounds can exceed 50 million total tokens before retries.
Better workflow
Ask for a repository map first, then focus on selected directories. For example, analyze routing, database access, authentication, and billing modules separately instead of sending the whole project for every question. Store summaries of stable modules and reuse them in later rounds.
When to use a stronger model
Use cheaper models for file classification, summary generation, and simple extraction. Use stronger models for cross-file reasoning, security-sensitive changes, migration plans, or final review. This model routing pattern often reduces cost without reducing reliability.