Processing 500 lines of code (~5,600 tokens) with NVIDIA Nemotron-4 340B costs $0.0084 for codebase ingestion and $0.0118 for an AI-powered code review and refactoring pass.
Feed 500 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 500 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
NVIDIA Nemotron-4 340B writes 500 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| NVIDIA Nemotron-4 340B (Current) | nvidia | $0.0084 | $0.0021 | $0.0118 | 128,000 |
| GPT-5.6 Sol | openai | $0.0255 | $0.00255 | $0.0561 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.0638 | $0.006375 | $0.1403 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.0255 | $0.00255 | $0.0561 | 512,000 |
| GPT-5.5 Pro | openai | $0.153 | $0.0153 | $0.3366 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $0.102 | $0.0102 | $0.1836 | 1,000,000 |
On average, code yields approximately 11.2 tokens per line in NVIDIA Nemotron-4 340B (tiktoken_cl100k tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 500 lines of code produces approximately 5,600 tokens.
Sending 500 lines of code as context and generating a thorough code review with recommendations costs approximately $0.0118. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.00546.
NVIDIA Nemotron-4 340B has a context window of 128,000 tokens. 500 lines of code consumes 4.38% of its total available context.