Processing 2,500 lines of code (~28,000 tokens) with OpenRouter Free Tier (Llama 3.3) costs $0.00 for codebase ingestion and $0.00 for an AI-powered code review and refactoring pass.
Feed 2,500 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 2,500 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
OpenRouter Free Tier (Llama 3.3) writes 2,500 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| OpenRouter Free Tier (Llama 3.3) (Current) | openrouter | $0.00 | $0.00 | $0.00 | 64,000 |
| GPT-5.6 Luna | openai | $0.0051 | $0.00051 | $0.0112 | 1,050,000 |
| GPT-5.4 mini | openai | $0.0191 | $0.001912 | $0.0421 | 256,000 |
| GPT-5.4 nano | openai | $0.0051 | $0.00051 | $0.0115 | 128,000 |
| GPT-4o mini | openai | $0.003825 | $0.001912 | $0.006885 | 128,000 |
| Claude Haiku 4.5 | anthropic | $0.028 | $0.0028 | $0.056 | 1,000,000 |
On average, code yields approximately 11.2 tokens per line in OpenRouter Free Tier (Llama 3.3) (tiktoken_cl100k tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 2,500 lines of code produces approximately 28,000 tokens.
Sending 2,500 lines of code as context and generating a thorough code review with recommendations costs approximately $0.00. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.00.
OpenRouter Free Tier (Llama 3.3) has a context window of 64,000 tokens. 2,500 lines of code consumes 43.75% of its total available context.