Processing 1,000 lines of code (~10,200 tokens) with o3-pro (Frontier Reasoning) costs $0.204 for codebase ingestion and $0.3672 for an AI-powered code review and refactoring pass.
Quick answer
A 1,000 lines of code codebase is estimated at 10,200 tokens for o3-pro (Frontier Reasoning). Ingestion costs $0.204; a review and refactoring pass with roughly 20% output costs about $0.3672.
Method & trust
Code is estimated at a tokenizer-specific tokens-per-line ratio, then priced separately for repository context and generated review output. Comments, minified files, tests, and repeated context can materially change the bill.
Feed 1,000 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 1,000 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
o3-pro (Frontier Reasoning) writes 1,000 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| o3-pro (Frontier Reasoning) (Current) | openai | $0.204 | $0.0204 | $0.3672 | 1,000,000 |
| GPT-5.6 Sol | openai | $0.051 | $0.0051 | $0.1122 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.1275 | $0.0128 | $0.2805 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.051 | $0.0051 | $0.1122 | 512,000 |
| GPT-5.5 Pro | openai | $0.306 | $0.0306 | $0.6732 | 512,000 |
| o3 (Reasoning Frontier) | openai | $0.102 | $0.0102 | $0.1836 | 1,050,000 |
On average, code yields approximately 10.2 tokens per line in o3-pro (Frontier Reasoning) (o200k_base tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 1,000 lines of code produces approximately 10,200 tokens.
Sending 1,000 lines of code as context and generating a thorough code review with recommendations costs approximately $0.3672. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.1836.
o3-pro (Frontier Reasoning) has a context window of 1,000,000 tokens. 1,000 lines of code consumes 1.02% of its total available context.