Processing 5,000 lines of code (~56,000 tokens) with Qwen3.5 397B A17B (OpenRouter) costs $0.0218 for codebase ingestion and $0.048 for an AI-powered code review and refactoring pass.
Feed 5,000 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 5,000 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
Qwen3.5 397B A17B (OpenRouter) writes 5,000 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| Qwen3.5 397B A17B (OpenRouter) (Current) | qwen | $0.0218 | $0.00546 | $0.048 | 262,144 |
| GPT-5.6 Sol | openai | $0.255 | $0.0255 | $0.561 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.6375 | $0.0638 | $1.402 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.255 | $0.0255 | $0.561 | 512,000 |
| GPT-5.5 Pro | openai | $1.53 | $0.153 | $3.366 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $1.02 | $0.102 | $1.836 | 1,000,000 |
On average, code yields approximately 11.2 tokens per line in Qwen3.5 397B A17B (OpenRouter) (qwen_bpe tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 5,000 lines of code produces approximately 56,000 tokens.
Sending 5,000 lines of code as context and generating a thorough code review with recommendations costs approximately $0.048. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.0317.
Qwen3.5 397B A17B (OpenRouter) has a context window of 262,144 tokens. 5,000 lines of code consumes 21.36% of its total available context.