A standard 1 page document (~500 words / 667 tokens) costs $0.00012 to ingest and $0.000415 to generate a comprehensive executive summary using Llama 3.3 70B Instruct.
Feed the entire 1 page document into context to extract fields, entities, or answer queries.
Ingest 1 page and generate a structured 500-token executive summary with key takeaways.
Translate or reformat the entire 1 page document into an equivalent length output.
| Model | Provider | Ingestion (1 page) | Cached Ingestion | Summary Cost | Context Limit |
|---|---|---|---|---|---|
| Llama 3.3 70B Instruct (Current) | meta | $0.00012 | $0.000030 | $0.000415 | 128,000 |
| GPT-5.6 Terra | openai | $0.00128 | $0.000128 | $0.00728 | 1,050,000 |
| GPT-5.4 Workhorse | openai | $0.0016 | $0.00016 | $0.0091 | 256,000 |
| o3-mini | openai | $0.000704 | $0.000352 | $0.002904 | 200,000 |
| o4-mini | openai | $0.000704 | $0.000176 | $0.002904 | 256,000 |
| o1-mini | openai | $0.000704 | $0.000352 | $0.002904 | 128,000 |
Assuming a standard single-spaced document with ~500 words per page, a 1 page document contains approximately 500 words, which translates to roughly 667 tokens using Llama 3.3 70B Instruct's tokenizer. Dense PDF tables, legal boilerplate, and OCR scans may increase this by 20–30%.
Llama 3.3 70B Instruct has a context window of 128,000 tokens. A 1 page document consumes only 0.52% 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 $0.000415. Using 24-hour async batch API queues drops this cost to $0.000208.
If your workflow repeatedly queries or chats with this same 1 page document, prompt caching reduces subsequent turn input costs by up to 90% (to $0.000030 per turn instead of $0.00012).