A standard 500 pages document (~250,000 words / 320,000 tokens) costs $0.064 to ingest and $0.0646 to generate a comprehensive executive summary using GPT-5.6 Luna Pro (OpenRouter).
Quick answer
A 500 pages document is modeled as 250,000 words or 320,000 tokens. With GPT-5.6 Luna Pro (OpenRouter), ingestion costs $0.064; a 500-token executive summary brings the modeled total to $0.0646.
Method & trust
The page uses approximately 500 words per page, estimates tokens with the selected model tokenizer, and prices input plus a 500-token summary at the model's listed rates. OCR, tables, and formatting can increase the actual count.
Feed the entire 500 pages document into context to extract fields, entities, or answer queries.
Ingest 500 pages and generate a structured 500-token executive summary with key takeaways.
Translate or reformat the entire 500 pages document into an equivalent length output.
| Model | Provider | Ingestion (500 pages) | Cached Ingestion | Summary Cost | Context Limit |
|---|---|---|---|---|---|
| GPT-5.6 Luna Pro (OpenRouter) (Current) | openai | $0.064 | $0.0064 | $0.0646 | 1,050,000 |
| GPT-5.6 Sol | openai | $1.60 | $0.16 | $1.615 | 1,050,000 |
| GPT-5.6 Cyber | openai | $4.00 | $0.40 | $4.038 | 1,050,000 |
| GPT-5.5 Standard | openai | $1.60 | $0.16 | $1.615 | 512,000 |
| GPT-5.5 Pro | openai | $9.60 | $0.96 | $9.69 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $6.40 | $0.64 | $6.44 | 1,000,000 |
Assuming a standard single-spaced document with ~500 words per page, a 500 pages document contains approximately 250,000 words, which translates to roughly 320,000 tokens using GPT-5.6 Luna Pro (OpenRouter)'s tokenizer. Dense PDF tables, legal boilerplate, and OCR scans may increase this by 20–30%.
GPT-5.6 Luna Pro (OpenRouter) has a context window of 1,050,000 tokens. A 500 pages document consumes only 30.48% of its total window, easily fitting in a single prompt without requiring chunking or vector search.
Ingesting the document and outputting a concise 500-token executive summary costs $0.0646. Using 24-hour async batch API queues drops this cost to $0.0323.
If your workflow repeatedly queries or chats with this same 500 pages document, prompt caching reduces subsequent turn input costs by up to 90% (to $0.0064 per turn instead of $0.064).