Processing 50 lines of code (~560 tokens) with Llama 4 Scout (109B MoE) costs $0.000084 for codebase ingestion and $0.000134 for an AI-powered code review and refactoring pass.
Feed 50 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 50 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
Llama 4 Scout (109B MoE) writes 50 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| Llama 4 Scout (109B MoE) (Current) | meta | $0.000084 | $0.000021 | $0.000134 | 10,000,000 |
| GPT-5.6 Terra | openai | $0.00102 | $0.000102 | $0.002244 | 1,050,000 |
| GPT-5.4 Workhorse | openai | $0.001275 | $0.000128 | $0.002805 | 256,000 |
| o3-mini | openai | $0.000561 | $0.000281 | $0.00101 | 200,000 |
| o4-mini | openai | $0.000561 | $0.00014 | $0.00101 | 256,000 |
| o1-mini | openai | $0.000561 | $0.000281 | $0.00101 | 128,000 |
On average, code yields approximately 11.2 tokens per line in Llama 4 Scout (109B MoE) (tiktoken_cl100k tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 50 lines of code produces approximately 560 tokens.
Sending 50 lines of code as context and generating a thorough code review with recommendations costs approximately $0.000134. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.000071.
Llama 4 Scout (109B MoE) has a context window of 10,000,000 tokens. 50 lines of code consumes 0.01% of its total available context.