A standard 500 pages document (~250,000 words / 333,250 tokens) costs $0.15 to ingest and $0.1506 to generate a comprehensive executive summary using Llama 4 Maverick (400B MoE).
Quick answer
A 500 pages document is modeled as 250,000 words or 333,250 tokens. With Llama 4 Maverick (400B MoE), ingestion costs $0.15; a 500-token executive summary brings the modeled total to $0.1506.
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 |
|---|---|---|---|---|---|
| Llama 4 Maverick (400B MoE) (Current) | meta | $0.15 | $0.0375 | $0.1506 | 1,000,000 |
| 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 333,250 tokens using Llama 4 Maverick (400B MoE)'s tokenizer. Dense PDF tables, legal boilerplate, and OCR scans may increase this by 20–30%.
Llama 4 Maverick (400B MoE) has a context window of 1,000,000 tokens. A 500 pages document consumes only 33.32% 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 $0.1506. Using 24-hour async batch API queues drops this cost to $0.0753.
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 $0.0375 per turn instead of $0.15).