Processing 1,000 lines of code (~11,200 tokens) with Llama 3.3 70B Instruct costs $0.002016 for codebase ingestion and $0.003338 for an AI-powered code review and refactoring pass.
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.
Llama 3.3 70B Instruct writes 1,000 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| Llama 3.3 70B Instruct (Current) | meta | $0.002016 | $0.000504 | $0.003338 | 128,000 |
| GPT-5.6 Terra | openai | $0.0204 | $0.00204 | $0.0449 | 1,050,000 |
| GPT-5.4 Workhorse | openai | $0.0255 | $0.00255 | $0.0561 | 256,000 |
| o3-mini | openai | $0.0112 | $0.00561 | $0.0202 | 200,000 |
| o4-mini | openai | $0.0112 | $0.002805 | $0.0202 | 256,000 |
| o1-mini | openai | $0.0112 | $0.00561 | $0.0202 | 128,000 |
On average, code yields approximately 11.2 tokens per line in Llama 3.3 70B Instruct (tiktoken_cl100k tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 1,000 lines of code produces approximately 11,200 tokens.
Sending 1,000 lines of code as context and generating a thorough code review with recommendations costs approximately $0.003338. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.001826.
Llama 3.3 70B Instruct has a context window of 128,000 tokens. 1,000 lines of code consumes 8.75% of its total available context.