A standard 250 pages document (~125,000 words / 166,625 tokens) costs $0.0583 to ingest and $0.0588 to generate a comprehensive executive summary using Together AI — Llama 4 Maverick.
Quick answer
A 250 pages document is modeled as 125,000 words or 166,625 tokens. With Together AI — Llama 4 Maverick, ingestion costs $0.0583; a 500-token executive summary brings the modeled total to $0.0588.
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 250 pages document into context to extract fields, entities, or answer queries.
Ingest 250 pages and generate a structured 500-token executive summary with key takeaways.
Translate or reformat the entire 250 pages document into an equivalent length output.
| Model | Provider | Ingestion (250 pages) | Cached Ingestion | Summary Cost | Context Limit |
|---|---|---|---|---|---|
| Together AI — Llama 4 Maverick (Current) | together | $0.0583 | $0.0146 | $0.0588 | 1,000,000 |
| GPT-5.6 Sol | openai | $0.80 | $0.08 | $0.815 | 1,050,000 |
| GPT-5.6 Cyber | openai | $2.00 | $0.20 | $2.038 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.80 | $0.08 | $0.815 | 512,000 |
| GPT-5.5 Pro | openai | $4.80 | $0.48 | $4.89 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $3.20 | $0.32 | $3.24 | 1,000,000 |
Assuming a standard single-spaced document with ~500 words per page, a 250 pages document contains approximately 125,000 words, which translates to roughly 166,625 tokens using Together AI — Llama 4 Maverick's tokenizer. Dense PDF tables, legal boilerplate, and OCR scans may increase this by 20–30%.
Together AI — Llama 4 Maverick has a context window of 1,000,000 tokens. A 250 pages document consumes only 16.66% 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 $0.0588. Using 24-hour async batch API queues drops this cost to $0.0294.
If your workflow repeatedly queries or chats with this same 250 pages document, prompt caching reduces subsequent turn input costs by up to 90% (to $0.0146 per turn instead of $0.0583).